Digital teaching methods in anatomy courses in US and Canadian medical schools
Bibliographic record
Abstract
Objective To determine current and planned future use of radiological and digital teaching methods in anatomy courses. Methods A survey was sent to AAMC‐listed medical schools, regarding design, duration, topics covered, and use of radiology and digital histologic slides in the anatomy course. Results 60 schools responded (50%). Most time is spent on dissection (51%) and lectures (27%). Radiologic anatomy averages 4.5% of course time. Most schools plan to reduce anatomy teaching time in the next 5 years; lectures, dissections, and prosections will decrease, while digital teaching methods will increase. Anatomy courses that teach histology spend 61% of the time using microscopes, 17% using the web, and 17% using digital slides. In the next 5 years, microscope use will be halved and digital slide use will double. Gross lab practicals and radiology exam questions are tested at most schools. Conclusion Most schools will decrease total hours spent teaching anatomy over the next 5 years without changing time devoted to radiology. Although imaging is used in most schools’ (49) exams, it comprises a small percentage of current and projected teaching time (4.5%). Given the value of radiologic anatomy to patient care, imaging should increasingly play a larger role in teaching anatomy. The trend toward digital histology teaching suggests computer infrastructure for imaging may already be in place or planned for the future.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".