Using Clinical Evaluation and Lung Scan to Rule Out Suspected Pulmonary Embolism
Bibliographic record
Abstract
BACKGROUND: In patients with a low clinical probability of pulmonary embolism (PE) and a nondiagnostic lung scan, the prevalence of PE is theoretically very low. We assessed the safety and usefulness of this association for ruling out PE. METHODS: We analyzed data from 2 consecutive cohort management studies performed in 2 university hospitals (Geneva University Hospital, Geneva, Switzerland, and Hospital Saint-Luc, Montreal, Quebec), which enrolled 1034 consecutive patients who came to the emergency department with clinically suspected PE. All patients were submitted to a sequential diagnostic protocol of lung scan, D-dimer testing, lower-limb venous compression ultrasonography (US), and pulmonary angiography in case of inconclusive results of noninvasive workup. RESULTS: The prevalence of PE was 27.6%. Empirical assessment was accurate for identifying patients with a low likelihood of PE (8.2% prevalence of PE in the low clinical probability category). One hundred eighty patients had a low clinical probability of PE and a nondiagnostic lung scan. Among these patients, US showed deep vein thrombosis in 5. Hence, PE could be ruled out by a low clinical probability, a nondiagnostic lung scan, and a normal US in 175 patients (21.5%). The 3-month thromboembolic risk in these patients was low (1.7%; 95% confidence interval, 0.4%-4.9%). CONCLUSIONS: Anticoagulant treatment could be safely withheld in patients with a low clinical probability of PE and a nondiagnostic lung scan, provided that the US is normal. This combination of findings avoided pulmonary angiography in 21.5% of patients with suspected PE in this series.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".