Bibliographic record
Abstract
To the Editor: I commend Scheinman and colleagues’1 wise article “Oath Taking at U.S. and Canadian Medical School Ceremonies: Historical Perspectives, Current Practices, and Future Considerations.” The authors note that 18% of medical students recall at least three obligations in their school’s oath.2 This is consistent with my finding that 10% of physicians and medical students could correctly answer five questions about the contents of the Hippocratic Oath.3 Medical school oaths are sacred texts consisting of hallowed words—either because they are associated with the divine or because they are consecrated or deserving of reverence.4 Examples of sacred texts include the Bible, the Koran, the Gettysburg Address, and professional oaths. We venerate these texts and turn them into wall plaques and authority objects (putting your hand on a Bible when taking an oath, for example). Their contents, however, are often lost in the midst of our veneration. Few Americans can answer the simplest questions about the contents of the U.S. Constitution, for example.5 Scheinman and colleagues refer to the Hippocratic Oath as “attributed to Hippocrates.” They refer to the Oath of Maimonides, or a variant, without the modifier “attributed.”1 Moses Ben Maimon (1135–1204; in Greek, Maimonides) wrote neither the Physician’s Prayer of Maimonides nor the derived Oath of Maimonides.6 This prayer appeared in print in 1783 and was probably written by the Berlin physician Marcus Hertz (1747–1803).6 Attributing the prayer and the oath to Maimonides is a long-standing benign medical history hoax.6 Medical oaths deserve to be the subjects of continuing education and reaffirmation. This might help convert them from wall plaques to living documents. Edward C. Halperin, MD, MAChancellor and chief executive officer, New York Medical College, Valhalla, New York, and provost, Biomedical Affairs, Touro College, Valhalla, New York; [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 | 0.003 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.020 | 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".