Lines in the sand: pre-interview rank and probability of receiving admission to medical school
Bibliographic record
Abstract
Background: We provide an examination of one medical school’s attempt to determine whether their cut-off point for number of interviews offered is congruent with the probability these applicants’ have for admission post-interview. Methods: Offer probability was determined by organizing pre-interview rankings from 2013-2017 (n = 2,659) applicant cohorts into bins of 50 applicants and finding the quotient of successful and total applicants in each bin.A linear-by-linear association Chi-square test and adjusted standardized residuals with an applied Bonferroni correction were used to determine if the observed frequencies in each bin were different than expected by chance. A Spearman Correlation analysis between pre- and post-interview ranks was conducted. Results: All applicants have between a 50.0% and 76.4% chance of admission. Observed frequencies are different than chance (χ(1)=50.835, p<.001), with a significantly greater number of offers seen in the bins between 1 and 100 (p<.001 for both bins). There is a weak positive relationship between pre- and post-rank, rs(2657)= 0.258, p<.001. Conclusion: The results indicate the number of interviews conducted does not exceed a threshold wherein individuals with a relatively low chance of admission are interviewed. Findings are interpreted with respect to ethical resource allocation for both programs and applicants.
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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 | 0.002 | 0.022 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".