Tips for Teachers of Evidence-based Medicine: Making Sense of Decision Analysis Using a Decision Tree
Bibliographic record
Abstract
Decision analysis is a tool that clinicians can use to choose an option that maximizes the overall net benefit to a patient. It is an explicit, quantitative, and systematic approach to decision making under conditions of uncertainty. In this article, we present two teaching tips aimed at helping clinical learners understand the use and relevance of decision analysis. The first tip demonstrates the structure of a decision tree. With this tree, a clinician may identify the optimal choice among complicated options by calculating probabilities of events and incorporating patient valuations of possible outcomes. The second tip demonstrates how to address uncertainty regarding the estimates used in a decision tree. We field tested the tips twice with interns and senior residents. Teacher preparatory time was approximately 90 minutes. The field test utilized a board and a calculator. Two handouts were prepared. Learners identified the importance of incorporating values into the decision-making process as well as the role of uncertainty. The educational objectives appeared to be reached. These teaching tips introduce clinical learners to decision analysis in a fashion aimed to illustrate principles of clinical reasoning and how patient values can be actively incorporated into complex decision making.
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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 | 0.015 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".