When Guidelines Don’t Guide
Bibliographic record
Abstract
PURPOSE: This study examines the influence of patient social context on physicians' adherence to clinical practice guidelines (CPGs). METHOD: Expert emergency medicine (EM) physicians and novice physicians (EM residents) were surveyed using an Internet-based program between January and July of 2013. Participants were presented clinical cases and were asked to indicate if they would order or prescribe a specified test or treatment. Cases were chosen from four domains where CPGs exist, and were constructed to include or exclude a "context variable" (CV). Both expert and novice physicians' CPG adherence rate in the CV condition was compared with that in the no CV condition. The CPG adherence rates in CV and no CV conditions were also compared between expert and novice EM physicians. RESULTS: Expert EM physicians (n = 28) were less likely to adhere to CPGs in the CV condition compared with the no CV condition (56% versus 80%, respectively; odds ratio [OR] = 0.32, 95% confidence interval [CI]: 0.17-0.53, P < .001). Experts were less likely to adhere to CPGs in the CV condition when compared with novice physicians (n = 28) (56% versus 67%; OR = 0.62, 95% CI: 0.39-1.0, P = .039). Expert and novice EM physicians did not differ in their adherence to CPGs in the no CV condition. CONCLUSIONS: Participants were sensitive to both the best clinical evidence of benefit, as recommended by CPGs, and patient context when determining how care should be managed.
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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 | 0.033 | 0.236 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.019 | 0.010 |
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".