Family Medicine Residencys That Acceppt Low Comlex Scores

• Research article
• Open Access
• Published: 21 January 2021

Early prediction of the adventure of scoring lower than 500 on the COMLEX one

BMC Medical Education volume 21, Article number:70 (2021) Cite this article

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Abstract

Groundwork

The Comprehensive Osteopathic Medical Licensing Test of the United states of america Level ane (COMLEX 1) is important for medical students to exist able to graduate. There is a glaring demand to identify students who are at a significant gamble of performing poorly on COMLEX 1 as early every bit possible so that extra assistance tin be provided to those students. Our goal is to produce a reliable predictive model to identify students who are at take a chance of scoring lower than 500 on COMLEX i at the earliest possible time.

Methods

Academic information from medical students who matriculated at Rocky Vista University College of Osteopathic Medicine betwixt 2011 and 2017 were obtained. Odds ratios were used to assess the predictors for scoring lower than 500 on COMLEX ane. Correlation with COMLEX 1 scores was assessed with Pearson correlation coefficient. The predictive models were adult by multiple logistic regression, astern logistic regression, and logistic regression with average scores in courses in the first three semesters, and were based on performances on the Medical Higher Admissions Test (MCAT) before admission, likewise as students' performances in preclinical courses during the first three semesters. The models were generated in nearly 82% of the student performance data and were then validated in the remaining 18% of the data.

Results

Odds ratios showed that MCAT scores and final grades in each course in the first three semesters were significant in predicting a score lower than 500 on COMLEX one. Performances in 3rd-semester courses including Renal System Ii, Cardiovascular System II, and Respiratory Organisation II were most of import in prediction. The three predictive models had sensitivities of 65.viii -71%, and specificities of 83.2 - 88.2% in predicting a score lower than 500 on COMLEX 1.

Conclusions

Lower MCAT scores and lower grades in the first three semesters of medical school predict scoring lower than 500 on COMLEX 1. Students who are identified at risk by our models will take a 65.8 -71% chance of actually scoring lower than 500 on COMLEX 1. Those students will have enough fourth dimension to receive assistance before taking COMLEX 1.

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Introduction

Students enrolled in an osteopathic medical school must laissez passer the Comprehensive Osteopathic Medical Licensing Test of the U.s. Level 1 (COMLEX-U.s. Level 1 or COMLEX i) to be eligible to enroll in tertiary year clinical rotations at some schools, to be qualified to take COMLEX-United states Level 2, and eventually to graduate and receive a Doc of Osteopathic Medicine (DO) caste. Mitsouras et al. take observed a 4.8% rate of starting time-attempt failure on the COMLEX 1 among 1726 students at Western University of Health Scientific discipline betwixt 2010 and 2017 [1]. There is clear motivation for osteopathic medical schools to early on place those students who are at meaning take chances of failing or performing poorly on the COMLEX 1, so that actress assistance tin can be provided to those students through a variety of bookish support channels.

Although some studies accept been done to predict United States Medical Licensing Test (USMLE) Step 1 or Step ii exam performance for medical students, there are currently only a few studies that have attempted to predict COMLEX ane performance from various preadmission and postadmission academic data. The COMLEX-Usa examination is comparable to the allopathic licensing exam (USMLE) [2]. Preadmission variables that have been shown to positively correlate with COMLEX i score include undergraduate science grade signal average (sciGPA) and Medical Higher Admission Test (MCAT) score [2,3,4]. Very loftier scores on the MCAT are also correlated with a COMLEX I score of 600 (80th percentile) or higher [5]. Postadmission variables, including performance in first-year and second-year medical school courses, predict scores on COMLEX 1 every bit well [two, 4,5,vi]. In one written report of 2146 students, all students in the top 20% of the grade pass the COMLEX 1 on the first endeavor, whereas only 64% of students who are ranked in the lowest 5% in the class pass [7]. Performance in the discipline of pharmacology in an osteopathic medical school curriculum has also been institute to strongly correlate with operation on COMLEX 1 [8]. For the last 20 years, many allopathic medical schools, too as osteopathic medical schools, have implemented an organ-organisation based curriculum, but studies that have specifically connected an organ-system based curriculum and COMLEX i performance are rare [ix]. Glaros et al. have found that the highest correlation with scores on COMLEX 1 is 2d semester Renal department class amidst all the courses in the offset 2 years, as shown in one particular study of traditional organ-system curriculum [9]. Our school, Rocky Vista University College of Osteopathic Medicine, has a modified two-pass organ-system curriculum, which was initiated in 2011. Currently, it appears that there is no research studying the correlation betwixt students' performances in the modified organ-organization courses and COMLEX 1, a gap in the literature that we are hoping to fill up.

To aid all students achieve success, information technology is disquisitional to place students at take a chance of poor operation on COMLEX i early. A score of 500 on COMLEX one has been regarded equally national average for many years, but really the percentile corresponding to a score of 500 has gradually decreased since 2011, from 43th in 2011 to 36th in 2020, according to the National Board of Osteopathic Medical Examiners (NBOME). A score of 500 on COMLEX 1 is truly beneath national average. Since the failing score is 400, a student who scores lower than 500 on COMLEX 1 has high risk of failing COMLEX 1. The purposes of our current projection are to investigate the take a chance factors and generate reliable predictive models to identify students at the end of their third-semester who are at adventure of performing lower than 500 on COMLEX 1. As a result, those students will have at least 7 months to get extra assistance earlier they must take COMLEX 1. We hope that the early intervention will enhance at chance students' operation on COMLEX 1 and will help them avoid failing this test.

Methods

Curriculum

Rocky Vista University College of Osteopathic Medicine (RVUCOM) is in the United States, and has a modified systems-based curriculum, which requires students to cover each system twice, once in the outset year and over again in the 2d year. This ii laissez passer, stepwise curriculum focuses on normal construction and function in the first year and transitions to abnormal function in the 2d year, with increased accent on pathology, pharmacology, and clinical application. The majority of the coursework of the beginning three semesters (offset 1.5 years) is shown in Table ane.

Table 1 Odds Ratio with COMLEX-1 Score 500 equally Cutoff (N = 904)

Full size tabular array

Participants

Our research project of "Using Simulation Modeling to Predict Failure on COMLEX i and two at First Attempt Through a Longitudinal Investigation" was approved by IRB committee of Rocky Vista University College of Osteopathic Medicine (RVUCOM), and the IRB number was IRB #2019-0079. The waiver was obtained for informed consent from IRB committee of RVUCOM since IRB adamant the written report was exempt. Academic performance data from 7 cohorts (2011 to 2017) of students matriculated at Rocky Vista University College of Osteopathic Medicine were obtained. Students' data were de-identified past the University registrar before disclosure to the investigators.

Independent variables

Preadmission MCAT (the old version of the exam administered between 1991 and 2014) scores, and postadmission grades in each course in the beginning three semesters were used in this study. For students who took the MCAT more than than once, average scores on all MCAT attempts were used. Scores on the MCAT from 1991 to 2014 ranged from a minimum of 3 to a maximum of 45. The 50th percentile was around 25. Scores in each course at RVUCOM were on a 1000 point calibration reported in students' transcripts.

Dependent variable

De-identified COMLEX 1 scores on the showtime attempt for 904 students were nerveless.

Statistical analysis

Univariate logistic regression

Univariate logistic regression was used to generate odds ratios. Dependent upshot was score on COMLEX 1. All independent variables were included. A score of 500 or higher was fix to 0, and a score of 500 lower was set to 1. Threshold probability for positive classification was 0.v.

Bivariate correlations between variables

Pearson correlation coefficient (R) was used to mensurate the correlations between independent variables and dependent variable (score of COMLEX ane), and the correlations betwixt each independent variable.

Data for establishing and validating predictive models

Amongst 904 participants, some of the course grades were missing from the data. The 885 participants with complete data were randomly separated into a training data set up with 728 participants (nearly 82%), and a testing data fix with remaining 157 participants (most 18%). The models were developed with the training data set and were validated in the testing data set.

Model 1: multiple logistic regression

Contained variables included MCAT scores, and scores in each course in the first-iii semesters. Dependent variable was score on COMLEX 1. A COMLEX 1 score ≥ 500 was ready as 0, and a score of COMLEX 1 < 500 was set equally 1. All contained variables were included in the final formula. Two cutoff probability values were tried and compared to discover a better cutoff value.

A cutoff probability value of 0.v was tried first. A educatee with a predicted probability equal to or higher than 0.v is predicted to score below 500 on COMLEX i, and a student with a predicted probability lower than 0.5 is expected to score 500 or higher on COMLEX one.

A cutoff probability value of 0.25 was chosen after. A student with a predicted probability equal to or higher than 0.25 is predicted to score below 500 on COMLEX 1, and a pupil with a predicted probability lower than 0.25 is expected to score 500 or higher on COMLEX 1.

The sensitivity and specificity of prediction were compared between these ii cutoff probability values, and cutoff probability value of 0.25 led to better accuracy in predicting the fraction of participants who scored lower than 500 on COMLEX i. Therefore, the next two models were used with a cutoff probability value of 0.25 direct.

Model ii: astern stepwise logistic regression

Independent variables included MCAT scores, and scores in each grade in the first three semesters. Dependent variable was score on COMLEX 1. We set a COMLEX 1 score ≥ 500 equally 0, and < 500 as 1. Insignificant contained variables were removed sequentially until all variables were pregnant. The final formula contained just significant variables. A cutoff probability value of 0.25 was selected. A pupil with a predicted probability equal to or higher than 0.25 is predicted to score below 500 on COMLEX 1, and a student with a predicted probability lower than 0.25 is expected to score 500 or higher on COMLEX 1.

Model iii: logistic regression with boilerplate scores in all courses

In Model 3, the average score across all courses in the first 3 semesters was calculated for each student. This boilerplate score was used as a unmarried independent variable in a logistic model. Dependent variable was score on COMLEX 1. We set up a COMLEX 1 score ≥ 500 every bit 0, and < 500 as 1. As mentioned to a higher place, a cutoff value of 0.25 of probability was used; A student with a predicted probability equal to or higher than 0.25 is predicted to score below 500 on COMLEX i, and a educatee with a predicted probability lower than 0.25 is expected to score 500 or higher on COMLEX 1.

For each predictive model, the number of true positives (TP) (participants who were predicted to have a score lower than 500 on COMLEX ane who really had a score lower than 500), imitation negatives (FN) (participants who were predicted to have a score of 500 or college who actually had a score lower than 500), true negatives (TN) (participants who were predicted to accept a score of 500 or higher who actually scored 500 or higher), and false positives (FP) (participants who were predicted to have a score lower than 500 who actually scored 500 or higher) were determined. Sensitivity (TP/(TP + FN)), and specificity (TN/(TN + FP)) were calculated.

All analyses were run using either IBM SPSS (Version twenty, IBM SPSS Statistics, Chicago, IL), SigmaPlot 14 (Systat Software Inc., San Jose, CA), or SAS version 9.4 (SAS Found, Cary, NC). The receiver operating feature bend (ROC) and the probability success plot were generated with Python language in the testing data ready.

Results

Odds ratios of independent variables on a COMLEX ane score lower than 500

To investigate the prediction with each independent variable on a score of less than 500 on COMLEX 1, odds ratios were generated by applying logistic regression to each independent variable. The odds ratios for all contained variables were shown in Table ane. Lower scores in the MCAT, each course (except PCM Ii (Principles of Clinical Medicine II)), and average scores in all courses in the first three semesters were all significant in predicting a COMLEX 1 score lower than 500. For example, the odds ratio for Cardiovascular System course (CV 2) was 0.971, which meant that a 1-point decrease in a CV 2 score (on a grand-point calibration) will yield a two.9% increment in odds of scoring lower than 500 on the COMLEX 1. Alternatively, for each x-point (1%) reduction in a CV II score, the odds of getting a COMLEX ane score of 500 lower volition increase by 29%. Similarly, the odds ratio for the average score in all courses was 0.955, which meant that 1-point reduction in the average score in all courses, the odds of getting a COMLEX 1 score lower than 500 will increase past 4.5%. On the other hand, for each boosted 10-point (1%) in the average score, the odds of performing higher than 500 on COMLEX 1 will increase past 45%. Each course score was on a 1000-point scale throughout this report.

Bivariate correlation between independent variables and scores of COMLEX 1

As shown in Table 2, COMLEX 1 scores had a weak positive correlation with MCAT scores with a Pearson R of 0.eighteen (p < 0.05). COMLEX ane scores had moderate-high positive correlation with all form scores, ranging from 0.41 to 0.7 (p < 0.05). The correlations with COMLEX 1 scores were gradually increased throughout our first iii semester preclinical courses, if two clinical courses of OPP (Osteopathic Principles/Practices) and PCM (Principles of Clinical Medicine) were non considered. The third semester Renal Arrangement (REN Ii) and Cardiovascular System II (CVII) had the highest correlation with COMLEX 1, with a Pearson R of 0.7. In addition, course scores were significantly positively correlated with each other, ranging from 0.29 to 0.77 (p < 0.05). The correlations with MCAT scores were weak with first semester courses, ranging from 0.07 to 0.24, and were much weaker with the 2d and third semester courses, ranging from 0 to 0.12.

Table two Bivariate Correlation of Independent Variables and Scores of COMLEX ane in the Outset Three Semesters (N = 904)

Full size tabular array

Logistic regression models

The formulas of three logistic regression models were shown in Table 3.

Tabular array three Formula of Logistic Regression Models to Predict COMLEX 1

Full size table

Multiple logistic regression model had 21 variables in the formula as shown in Tabular array 3. Equally shown in Table iv, when using a cutoff probability value of 0.v, multiple logistic regression model yielded a sensitivity of 48.two% in the grooming information set, and of 44.7% in the testing information set up. When the cutoff value was inverse to 0.25, the sensitivity was increased to 75.9% in the training data prepare, and 68.4% in the testing information ready. Additionally, with the cutoff value of 0.25, the testing data fix had a specificity of 88.2%, which was close to the specificity of 92.4% with the cutoff value of 0.five. Therefore, all three models adopted cutoff probability value of 0.25 because of better accuracy of prediction.

Table four Logistic Regression Models for Detecting Students Scoring Less Than 500 on COMLEX 1

Total size table

As shown in Table 3, the backward stepwise logistic regression model had four meaning variables left in the concluding formula. Scores in Cardiovascular Organization I (CV I), Cardiovascular System 2 (CV II), Renal System 2 (REN II) and Respiratory Organization Ii (RESP II) were significant in predicting COMLEX I scores lower than 500 in this model. As shown in Tabular array 4, this model had a sensitivity of 79.four% and a specificity of 80.1% in preparation information set, which is comparable to the multiple logistic regression model. The reduced number of variables in this model did non decrease the accurateness of prediction. The prediction accuracy of backward logistic regression model was validated in the testing data set, which yielded a sensitivity of 65.viii%, and a specificity of 88.two%. This was visualized in Fig. 1. Effigy 1 showed at probability of ane (respective to scoring lower than 500 on COMLEX 1), 25 out of 38 students who actually scored lower than 500 on COMLEX 1 were identified, and at probability of 0 (respective to scoring college than 500 on COMLEX 1), 105 out of 119 students whose bodily COMLEX ane scores college than 500 were detected.

Fig. ane

Probability Success Plot of Backward Logistic Regression Model. This is the validation of the backward logistic regression model in the testing data prepare. Probability of 1 means scores lower than 500 on COMLEX 1, and probability of 0 represents scores equal or higher than 500 on COMLEX 1. The blue dots represent the true COMLEX 1 scores, and red dots represent the predicted COMLEX 1 matched to truthful COMLEX 1 scores

Full size prototype

Every bit shown in Table 4, the logistic regression model with average scores in all courses identified 134 out of 170 participants who really scored lower than 500 on COMLEX 1 (sensitivity 78.8%), and 430 out of 558 participants who really scored higher than 500 (specificity 77%) in the training information set. In the testing data set, this model had a sensitivity of 27/38 (71%), and a specificity of 99/119 (83.2%).

To compare the accurateness of prediction among the three models, the receiver operating characteristic curve (ROC) of the three Models is shown in Fig. 2. The ROC curves of the three models were overlaid on each other, and had very like area ranging from 0.85658 to 0.86875. The backward logistic regression model had the largest surface area, and model of the logistic regression with average scores had the smallest area. Therefore, backward logistic regression model was the all-time to predict a COMLEX i score lower than 500.

Fig. 2

Receiver Operating Feature Curve (ROC) of Three Models. ROC measures the true positive rate and false positive rate at all values of probability. The expanse under the ROC curve evaluates model classification accurateness; the higher the area, the bigger the disparity between true and false positives, and thus the stronger the model in classifying members. The bluish colored curve was for the multiple logistic regression model whose area under the curve was 0.85984. The red colored curve was the backward logistic regression model, which has an surface area nether the bend of 0.86875. The yellow colored bend was for the logistic regression model with average scores in all courses, and its surface area under the bend was 0.85658

Full size image

Discussion

Our written report found that MCAT scores and scores in each form in the outset three semesters were all significant in predicting COMLEX 1 scores lower than 500. The multiple logistic regression model, astern stepwise logistic regression model, and the logistic regression model with average scores identified 65.8 -71% of students who actually scored lower than 500 on COMLEX one at their beginning attempt.

From our results, a low MCAT score was a weak only significant predictor of scoring lower than 500 on COMLEX 1. This is consistent with the literature, in which MCAT scores take been establish to positively correlate with COMLEX one operation [3, 4, ten]. Additionally, Vora et al. have found that students with COMLEX one scores of 600 (80th percentile) or college are 1.3 times more likely to have a college MCAT score [5]. Similarly, Gauer et al. have demonstrated that a MCAT score lower than 28 (66.8th percentile) predicts a USMLE Step 1 score lower than 207 (12th percentile), and a MCAT score higher than 40 (99.8th percentile) predicts a USMLE Step 1 score of 260 (96th percentile) or higher [xi]. Our study and the literature provide evidence that the MCAT score is still an of import criterion for selecting medical educatee candidates, in terms of predicting success on standardized board examinations.

Lower performance in each grade except PCM Two course in the first three semesters at RVUCOM was a predictor of a COMLEX 1 score lower than 500 in our report. Among all courses, Renal System Ii Course (REN Two), Cardiovascular Organisation I (CV I) and II Courses (CV Ii), and Respiratory Organisation II Course (RESP II) were the strongest predictors, according to the correlation coefficients and the backward stepwise logistic regression. Similar to our findings, Glaros et al. besides have identified that the 2d semester Renal section course is the number one predictor for COMLEX 1 scores among all preclinical courses in a traditional organ system curriculum at their institution [9]. In our study, Renal Organisation 2 (REN Two), Cardiovascular System II (CV 2), and Respiratory System Ii (RESP Ii) are courses in the third-semester. REN Ii course is implemented at the beginning of the second yr, and is followed past CVII and RESP Ii. Information technology seems that course performance in the 3rd semester, at the start of the 2d year, is most of import in predicting COMLEX 1 scores lower than 500. In that location is currently no explanation in the literature as to why these courses are so of import for functioning on COMLEX 1. The authors postulate that renal, cardiovascular, respiratory system courses involve understanding and heavy integration of anatomy, physiology, pathology, and pharmacology, all of which are heavily tested on COMLEX 1.

To predict low performance on COMLEX one early on, we developed three models: multiple logistic regression, astern stepwise logistic regression and logistic regression with average scores in all course in the first three semesters. The three models had very close sensitivities and specificities. Sensitivity and specificity for each model were similar between the training data set up and the testing data set. This means that each model is reliable for prediction of COMLEX scores lower than 500 and identification of students at take chances. Among the three models, the backward logistic regression model was the best in term of accuracy of prediction. Considering course scores were positively correlated with each other, backward logistic regression was better than multiple logistic regression to minimize the influence of collinearity. According to our models, if a educatee is predicted past our models to score lower than 500 on COMLEX one, this student will have a 65.viii -71% chance that he or she will actually score lower than 500. Once students are identified to be at risk of a poor COMLEX i operation, those students will nevertheless have at least vii months (5 months of the fourth-semester, plus 2 or three more than months to gear up) before taking COMLEX 1. Thus, they volition have time to accommodate their study patterns and to focus on the content they demand to main. Also, schools will have time to provide extra assistance to aid those students.

To our noesis, our study is the kickoff to utilise the start three semesters of preclinical courses to predict a COMLEX i score lower than 500. Compared to any other models congenital at the terminate of the 2d year or after the fourth semester, our current models have the advantage of letting students who are at risk of poor functioning on COMLEX 1 have enough time to modify their study strategy and receive assistance earlier they must take COMLEX 1.

Our study has limitations. Our report used scores from the sometime MCAT, the 1991-2014 version of the test. Since 2015, the new MCAT has gradually replaced the quondam MCAT, and the score scaling is different on the new test. Therefore, to compare an old MCAT score with a new MCAT score, the same percentile can be used [12]. Students in the 50th percentile received a score of approximately 25 in old MCAT scores, which is comparable to 500 in new MCAT Scaled Scores [12]. In improver, other medical schools may have different curriculum than RVUCOM, then our predictive models may not apply to other medical schools. Some medical schools take converted to a pass/fail grading system recently [xiii], therefore our models may not work in schools with this new grading arrangement.

In determination, lower MCAT scores and lower scores in preclinical courses are significant predictors of a COMLEX 1 score lower than 500. Performances on 3rd semester courses including Renal System II, Cardiovascular Organization II, and Respiratory System 2, are the superlative predictors of poor operation on COMLEX 1. Our 3 predictive models, based on MCAT scores and student functioning in courses in the first iii semesters, have similar accuracy in predicting poor functioning on COMLEX 1, just the backward logistic regression model turns out to be the best among the three models. Our models take the reward of early prediction, giving students plenty fourth dimension to better fix for COMLEX 1. In the futurity, studies are needed to explore new predictive modeling using the new version of the MCAT and a new pass/fail grading curriculum.

Availability of information and materials

All data used in the study are just bachelor for interested researchers upon request from the corresponding author after approval from the Institutional Review Board at RVU.

Abbreviations

COMLEX one:

The Comprehensive Osteopathic Medical Licensing Exam of the United States Level 1

MCAT:

The Medical College Admissions Test (MCAT)

SciGPA:

Undergraduate science grade point average

RVUCOM:

Rocky Vista University College of Osteopathic Medicine

CV I:

Cardiovascular System I form

CV Ii:

Cardiovascular Arrangement II course

REN II:

Renal System II form

RESP Two:

Respiratory Arrangement II course

References

1. Mitsouras K, Dong F, Safaoui MN, et al. Student academic performance factors affecting matching into outset-pick residency and competitive specialties. BMC Med Educ. 2019;19:241 https://doi.org/10.1186/s12909-019-1669-9.

   Article  Google Scholar

2. Cope MK, Baker HH, Fisk RO, Gorby JN, Foster RW. Prediction of student operation on the comprehensive osteopathic medical licensing examination level I based on admission data and course performance. J Am Osteopath Assoc. 2001;101(2):84–90 PMID: 11293374. https://pubmed.ncbi.nlm.nih.gov/11293374/.

   Google Scholar

3. Dixon D. Prediction of osteopathic medical school performance on the basis of MCAT score, GPA, sexual activity, undergraduate major, and undergraduate institution. J Am Osteopath Assoc. 2012;112(4):175–81 PMID: 22522516. https://www.ncbi.nlm.nih.gov/pubmed/22522516.

   Google Scholar

4. Agahi F, Speicher MR, Cisek G. Clan betwixt undergraduate functioning predictors and academic and clinical performance of osteopathic medical students. J Am Osteopath Assoc. 2018;118(2):106–14. https://doi.org/10.7556/jaoa.2018.025.

   Article  Google Scholar

5. Vora A, Maltezos N, Alfonzo 50, Hernandez N, Calix E, Fernandez MI. Predictors of scoring at least 600 on COMLEX-The states level one: successful preparation strategies. J Am Osteopath Assoc. 2013;113(2):164–73. https://doi.org/ten.7556/jaoa.2013.113.2.164.

   Article  Google Scholar

6. Baker HH, Cope MK, Fisk R, Gorby JN, Foster RW. Relationship of preadmission variables and beginning- and 2d-year class operation to performance on the National Board of osteopathic medical Examiners' COMLEX-USA level i examination. J Am Osteopath Assoc. 2000;100(3):153–61 PMID: 10763309. https://pubmed.ncbi.nlm.nih.gov/10763309/.

   Google Scholar

7. Baker HH, Foster RW, Bates BP, et al. Human relationship between academic achievement and COMLEX-USA level 1 performance: a multisite study. J Am Osteopath Assoc. 2000;100(four):238–42 PMID: 10808669. https://pubmed.ncbi.nlm.nih.gov/10808669/.

   Google Scholar

8. Sefcik DJ, Prozialeck WC, O'Hare TH. Characteristics of the courses that best predict COMLEX-U.s. level 1 functioning. J Am Osteopath Assoc. 2003;103(ten):491–4 PMID: 14620085. https://pubmed.ncbi.nlm.nih.gov/14620085/.

   Google Scholar

9. Glaros AG, Hanson A, Adkison LR. Early prediction of medical student performance on initial licensing examinations. Med.Sci.Educ. 2014;24:291–5. https://doi.org/ten.1007/s40670-014-0053-y.

   Article  Google Scholar

10. Wong SK, Ramirez JR, Helf SC. Student functioning on levels i and ii-CE of COMLEX-U.s.a.: do elective upper-level undergraduate science courses matter? J Am Osteopath Assoc. 2009;109(11):592–8 Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/19948693.

    Google Scholar

11. Gauer JL, Wolff JM, Jackson JB. Do MCAT scores predict USMLE scores? An analysis on 5 years of medical educatee information. Med Educ Online. 2016;21:31795. https://doi.org/10.3402/meo.v21.31795.

    Article  Google Scholar

12. AAMC. Percentile ranks for the MCAT exam. https://students-residents.aamc.org/advisors/commodity/percentile-ranks-for-the-mcat-examination/. Accessed on 8 Aug 2020.

13. Kim South, George P. The relationship between preclinical grading and USMLE scores in The states allopathic medical schools. Fam Med. 2018;50(2):128–31. https://doi.org/ten.22454/FamMed.2018.145163.

    Commodity  Google Scholar

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Acknowledgements

Not applicative.

Funding

The study received no funding from any source.

Author data

Affiliations

1. Rocky Vista University College of Osteopathic Medicine, 255 E. Center St, Ivins, UT, 84738, The states

   Qing Zhong, Matthew Linton & Marking Payton

2. Shenzhen DJI Sciences and Technologies Ltd, Shen Zhen, People's republic of china

   Han Wang

3. Rocky Vista Academy College of Osteopathic Medicine, Ivins, Us

   Payton Christensen & Kevin McNeil

Contributions

QZ, ML, and MP designed the project; QZ analyzed and interpreted information, prepared the manuscript; HW cleaned raw information, analyzed data and generated figures; PC and KM cleaned raw data and revised manuscript; ML substantially revised manuscript; MP analyzed and interpreted information, and revised manuscript; All authors read and approved the final manuscript.

Authors' information

Qing Zhong, Dr., PhD, Associate Professor of Pharmacology, Biomedical Science Section, Rocky Vista University, USA.

Matthew Linton, PhD, Professor of Physiology, Rocky Vista University, USA.

Mark Payton, PhD, Professor of Statistics, Chair of Biomedical Scientific discipline, Rocky Vista University, USA.

Payton Christensen, OMSII, Rocky Vista University Higher of Osteopathic Medicine, United states.

Kevin McNeil, OMSII, Rocky Vista University College of Osteopathic Medicine, USA.

Han Wang, Primary in Data Science, Data Analyst, Shenzhen DJI Sciences and Technologies Ltd., Shen Zhen, People's republic of china.

Respective author

Correspondence to Qing Zhong.

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Ethics approval and consent to participate

Ethical approval statement: Our enquiry project of "Using Simulation Modeling to Predict Failure on COMLEX 1 and 2 at First Try Through a Longitudinal Investigation" was approved by IRB commission of Rocky Vista University College of Osteopathic Medicine (RVUCOM), and the IRB number was IRB #2019-0079. IRB determined the report was exempt.

Accordance statement for guidelines: All procedures performed in studies involving human participants were in accordance with the upstanding standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its afterwards amendments or comparable ethical standards.

Informed consent statement: The waiver was obtained for informed consent from IRB committee of RVUCOM.

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The authors declare no competing interests.

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Zhong, Q., Wang, H., Christensen, P. et al. Early on prediction of the take chances of scoring lower than 500 on the COMLEX 1. BMC Med Educ 21, seventy (2021). https://doi.org/ten.1186/s12909-021-02501-5

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• Received: 13 October 2020

• Accepted: 04 Jan 2021

• Published: 21 January 2021

• DOI : https://doi.org/10.1186/s12909-021-02501-5

Keywords

• COMLEX i: the comprehensive osteopathic medical licensing examination of the The states level 1
• MCAT: the medical college admissions test
• Cardiovascular arrangement course
• Renal system form
• Respiratory organization form
• Predictive model
• Score lower than 500 on COMLEX 1

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