Bibliographic record
Abstract
In Reply: Eaglen and Penn point out that baccalaureate–MD programs, by nature of their design, address many of the problems of premedical education which I and others have identified. They also note that since about one quarter of U.S. medical schools offer a baccalaureate–MD option, there is a fertile environment in which to compare the effects of this program with a traditional premedical education. Of course, one of the most difficult challenges will be to develop valid and reliable ways to determine whether students develop into “creative and independent thinkers who have the capability and passion to tackle the most important problems in medicine,” the goal I stressed in my May editorial. Margo et al raise the issue of how premedical requirements may influence the recruitment of students who are more or less likely to choose a certain career pathway. I agree that this is an important area in need of further research. Kahn reminds us that premedical education is one part of a continuum and that there is value in examining reform across the spectrum of medical education. And Pisano encourages those of us who write about the need for reform to “get started” as soon as possible, and recommends that the AAMC play a role. I agree with Pisano that wonderful ideas are not enough, but must be complemented by fine deeds. In addition to these letters, I received a number of informal responses, both verbal and written, to my call for renewed attention to the quality of premedical education. I am delighted to see that a discussion about reforming premedical education is gaining momentum. Steven L. Kanter, MD Editor, Academic Medicine, and vice dean, University of Pittsburgh School of Medicine, Pittsburgh, PA. Correspondence: Academic Medicine, 2450 N Street, NW, Washington, DC 20037; ([email protected]).
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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.030 | 0.044 |
| Insufficient payload (model declined to judge) | 0.026 | 0.018 |
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".