Commentary on robust, defensible, and fair: The AMEE guide to selection into medical school: AMEE Guide No. 153
Bibliographic record
Abstract
The AMEE Guide to Selection for medical school is a welcome addition that provides much sound advice and guidance. It employs a comprehensive framework and a number of innovations, international case studies, for instance. There are also some omissions that a future revision could usefully address. The key ones concern the evidence base for assessment of personal attributes by questionnaire or interview; conflation of two separable stages in selection, meeting minimum requirements for suitability, and discriminating between suitable candidates; how best to provide feedback to candidates; and the question of what counts as a fair, equitable approach to selection. Nevertheless, the new AMEE Guide (No 153) is well-aligned with the most recent Ottawa consensus statement on selection, and will make a good contribution to the development or revision of selection systems in medical schools.
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 | 0.097 | 0.424 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.021 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.013 | 0.008 |
| Research integrity | 0.061 | 0.082 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".