Bibliographic record
Abstract
In the 19th and early 20th centuries, obstetric simulators were widely used in medical schools to teach patient assessment skills and to allow students to learn and practice management of a wide range of conditions. Several types of simulators were manufactured, but one, known as the Budin-Pinard phantom, was specifically identified and recommended by J. Whitridge Williams of Johns Hopkins University in a paper he presented to the June 1898 meeting of the Association of American Medical Colleges. Obstetrics simulation became less popular as more women were encouraged to deliver in hospitals, providing trainees the opportunity to learn from actual patients. Today, though, simulation is undergoing a renaissance in obstetrics as a tool to improve learning and patient safety. In light of this shift, the authors examine the origins of simulation in obstetrics training, and specifically why Williams recommended the Budin-Pinard simulator in particular. They investigate the context of simulation in U.S. and Canadian obstetrics training generally up to the early 20th century and provide details about the Budin-Pinard simulator. Finally, the authors offer a discussion of how the Budin-Pinard simulator shaped obstetrics training in the 19th and early 20th centuries and how it can contribute to modern medical education.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".