Physician choices in pulmonary embolism testing
Bibliographic record
Abstract
BACKGROUND: Evidence-based guidelines advise excluding pulmonary embolism (PE) diagnosis using d-dimer in patients with a lower probability of PE. Emergency physicians frequently order computed tomography (CT) pulmonary angiography without d-dimer testing or when d-dimer is negative, which exposes patients to more risk than benefit. Our objective was to develop a conceptual framework explaining emergency physicians' test choices for PE. METHODS: We conducted a qualitative study using in-depth interviews of emergency physicians in Canada. A nonmedical researcher conducted in-person interviews. Participants described how they would test simulated patients with symptoms of possible PE, answered a knowledge test and were interviewed on barriers to using evidence-based PE tests. RESULTS: We interviewed 63 emergency physicians from 9 hospitals in 5 cities, across 3 provinces. We identified 8 domains: anxiety with PE, barriers to using the evidence (time, knowledge and patient), divergent views on evidence-based PE testing, inherent Wells score problems, the drive to obtain CT rather than to diagnose PE, gestalt estimation artificially inflating PE probability, subjective reasoning and cognitive biases supporting deviation from evidence-based tests and use of evidence-based testing to rule out PE in patients who are very unlikely to have PE. Choices for PE testing were influenced by the disease, environment, test qualities, physician and probability of PE. INTERPRETATION: Analysis of structured interviews with emergency physicians provided a conceptual framework to explain how these physicians use tests for suspected PE. The data suggest 8 domains to address when implementing an evidence-based protocol to investigate PE.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".