Comparison of the Unstructured Clinician Estimate of Pretest Probability for Pulmonary Embolism to the Canadian Score and the Charlotte Rule: A Prospective Observational Study
Bibliographic record
Abstract
OBJECTIVES: Clinical decision rules have been validated for estimation of pretest probability in patients with suspected pulmonary embolism (PE). However, many clinicians prefer to use clinical gestalt for this purpose. The authors compared the unstructured clinical estimate of pretest probability for PE with two clinical decision rules. METHODS: This prospective, observational study was conducted from October 2001 to July 2004 at an urban academic emergency department with an annual census of 105,000. A total of 2,603 patients were enrolled; mean age (+/- SD) was 45 (+/- 16) years, and 70% were female. All patients were evaluated for PE using a previously published protocol, including D-dimer and alveolar dead space measurements, and selected use of pulmonary vascular imaging. All had 45-day follow-up. Interobserver agreement for each pretest probability estimation method was measured in a separate group of 154 patients. RESULTS: The overall prevalence of PE was 5.8% (95% confidence interval [CI] = 4.9% to 6.8%). Most were deemed low risk for PE, including 69% by the unstructured estimate < 15%, 73% by the Canadian score < 2, and 88% by the Charlotte rule "safe." The corresponding prevalence of disease in each of these low-risk groups was 2.6%, 3.0%, and 4.2%. Weighted Cohen's kappa values were 0.60 (95% CI = 0.46 to 0.74) for the unstructured clinical estimate < 15%, 0.47 (95% CI = 0.33 to 0.61) for the Canadian score < 2, and 0.85 (95% CI = 0.69 to 1.0) for the Charlotte rule "safe." CONCLUSIONS: The unstructured clinical estimate of low pretest probability for PE compares favorably with the Canadian score and the Charlotte rule. Interobserver agreement for the unstructured estimate is moderate.
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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 | 0.008 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".