The Predictive Value of Admission Qualifications on the Academic Performance of First-Year Medical Students in Pamantasan ng Lungsod ng Maynila (PLM) College of Medicine
Bibliographic record
Abstract
Well-designed admission criteria can predict the likelihood of students succeeding in the medical program. This study aims to evaluate the predictive capability of the admission qualifications used in the Pamantasan ng Lungsod ng Maynila (PLM) College of Medicine concerning the academic performance of first-year medical students. Data from 1,203 students were analyzed, revealing that premedical general weighted average (GWA) and National Medical Admission Test (NMAT) scores significantly correlate with academic performance in the first year, whereas Medical College Admission Test (MCAT) scores do not. The interview, when combined with GWA and NMAT scores, can also predict the students’ final GWA at the end of their first year. Premedical school and courses are also potential predictors of academic success. This study holds significant implications in refining the admission criteria in this university that would ensure academic success of the students.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".