Bibliographic record
Abstract
Various reports commissioned by the Carnegie Foundation for the Advancement of Teaching at the beginning of the 20th century surveyed the status of professional education in the United States and Canada, but none had as lasting or as deep an impact as Abraham Flexner's Medical Education in the United States and Canada. At the centennial of this seminal report, the author of this commentary examines the current manifestations of Flexner's proposed design for medical education and considers what modern-day knowledge should shape future reforms. Other articles in this commemorative issue propose changes for the next 100 years of American medical education, and in this commentary, the author suggests that new knowledge must be applied to existing education models to meet the needs of the next generation of patients and physicians. As medical education enters its second century after Flexner, the most fitting tribute is to build on his legacy, using the knowledge and experience gained to innovate in order to foster the profession's highest aims under new conditions and in new ways.
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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 | 0.011 | 0.064 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.037 | 0.046 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".