Commentary: Lowly Interns, More Is Merrier, and the Casablanca Strategy
Bibliographic record
Abstract
Test ordering is an integral part of clinical decision making. Variation in test-ordering behavior appears to reflect uncertainty in the clinical reasoning and decision-making process. Among decision makers, novices function mostly in the analytic mode of reasoning, experiencing high levels of uncertainty and, therefore, account for the most variance. While less discriminate test ordering has both economical and clinical downsides, it nevertheless remains a rite of passage along the road toward expertise. In response to the article by Iwashyna and colleagues, the author of this commentary reflects on the implications of test-ordering behavior in the academic medicine setting. The process of ordering tests can serve purposes other than the obvious, not the least of which allows the decision maker additional time for reflection in the decision-making process, perhaps leading to a less mindless and more mindful approach. The author observes that test-ordering behavior of novitiates might be optimized through a variety of strategies that improve both active and passive learning in the clinical environment. In addition to specific education around costs, as well as Bayesian considerations, active learning importantly requires exposure to those processes that may subvert clinical reasoning, notably cognitive biases. Passive learning is enhanced in supportive environments. Throughout, those who supervise and teach should provide effective models.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.055 | 0.044 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".