Understanding the Role of the Ottawa Ankle Rules in Physicians' Radiography Decisions: A Social Judgment Analysis Approach
Bibliographic record
Abstract
Clinical decision rules improve health care fidelity, benefit patients, physicians and healthcare systems, without reducing patient safety or satisfaction, while promoting cost-effective practice standards. It is critical to appropriately and consistently apply clinical decision rules to realize these benefits. The objective of this thesis was to understand how physicians use the Ottawa Ankle Rules to guide radiography decision-making. The study employed a clinical judgment survey targeting members of the Canadian Association of Emergency Physicians. Statistical analyses were informed by the Brunswik Lens Model and Social Judgment Analysis. Physicians’ overall agreement with the ankle rule was high, but can be improved. Physicians placed greatest value on rule-based cues, while considering non-rule-based cues as moderately important. There is room to improve physician agreement with the ankle rule and use of rule-based cues through knowledge translation interventions. Further development of this Lens Modeling technique could lend itself to a valuable cognitive behavioral intervention.
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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 | 0.019 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".