Predictive value of grade point average (GPA), Medical College Admission Test (MCAT), internal examinations (Block) and National Board of Medical Examiners (NBME) scores on Medical Council of Canada qualifying examination part I (MCCQE-1) scores
Bibliographic record
Abstract
BACKGROUND: To determine whether the pre-medical Grade Point Average (GPA), Medical College Admission Test (MCAT), Internal examinations (Block) and National Board of Medical Examiners (NBME) scores are correlated with and predict the Medical Council of Canada Qualifying Examination Part I (MCCQE-1) scores. METHODS: Data from 392 admitted students in the graduating classes of 2010-2013 at University of Manitoba (UofM), College of Medicine was considered. Pearson's correlation to assess the strength of the relationship, multiple linear regression to estimate MCCQE-1 score and stepwise linear regression to investigate the amount of variance were employed. RESULTS: Complete data from 367 (94%) students were studied. The MCCQE-1 had a moderate-to-large positive correlation with NBME scores and Block scores but a low correlation with GPA and MCAT scores. The multiple linear regression model gives a good estimate of the MCCQE-1 (R2 =0.604). Stepwise regression analysis demonstrated that 59.2% of the variation in the MCCQE-1 was accounted for by the NBME, but only 1.9% by the Block exams, and negligible variation came from the GPA and the MCAT. CONCLUSIONS: Amongst all the examinations used at UofM, the NBME is most closely correlated with MCCQE-1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".