Dedicating Speci.c Sessions of Cytopathology Courses to Medical Students
Bibliographic record
Abstract
OBJECTIVE: To establish a consensus among medical schools in North America on whether to dedicate specific sessions to teaching cytopathology to medical students. STUDY DESIGN: A list of all the medical schools in the United States, Canada and Puerto Rico was retrieved from the American Association of Medical Colleges Web site in conjunction with the information provided by the 33rd edition of the Directory of Pathology Training Programs, published by the Intersociety Committee on Pathology Information. A total of 147 schools were found. A questionnaire was designed to include 7 questions addressing this issue and was sent to each medical student pathology course director. RESULTS: Of the 147 questionnaires, 65 (44%) responses were received. Fifty-four (83%) indicated the total number of pathology lectures given to medical students in each course. The number of lectures ranged between 19 and 201, with a mean of 85. Seven (11%) stated that their systems used problem based learning and that therefore a specific number of pathology lectures could not be given accurately. Sixteen (25%) have cytology sessions incorporated in their pathology courses. Thirteen (20%) prefer to include cytopathology sessions in the course and are committed to doing so. Therefore, 29 (45%) institutions either have or prefer to have specific sessions dedicated to cytopathology education. CONCLUSION: Incorporating specific sessions dedicated to cytopathology education in the medical student curriculum is highly recommended. Using new educational techniques, including computer-based methods with real case studies, would add more educational value.
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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 | 0.025 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".