Criteria for including study of specific conditions during clerkship training in internal medicine
Bibliographic record
Abstract
A survey was conducted of physicians in practice, to determine the criteria that would lead the study of a particular condition to be an important component of clerkship training in internal medicine. Four such criteria were suggested: the prevalence of the condition in practice, the urgency with which it requires attention, the severity in terms of morbidity and mortality and the cost effectiveness of the intervention. The responses suggest that those in practice see prevalence, urgency and severity as criteria of almost equal weight, but place cost-effectiveness on a \nmuch lower priority. Unexpectedly, those who have been in practice many years are more concerned about the role of the cost-effectiveness of the intervention than recent graduates. Also specialists see this as being a more important criterion that family practitioners. Those with a faculty position in academic medicine have views which are similar to those who do not. Thus, in attempting to design the ideal clerkship, there is a widespread view that the role of cost effectiveness in treatment should receive a lower priority in determining curriculum content than prevalence, urgency or severity. On the basis of these data, the request that there be increased weight of cost-effectiveness in determining \ncurriculum content, will not receive strong endorsement from those in practice. \nKeywords: CLERKSHIP,CORE CURRICULUM, COST-EFFECTIVENESS, INTERNAL MEDICINE, PREVALENCE, \nSEVERITY, SURVEY, URGENCY
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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 | 0.030 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".