Derivation and Validation of a COPD-specific Pulmonary Embolism Diagnostic Strategy
Bibliographic record
Abstract
Introduction: Diagnosing pulmonary embolism (PE) in patients with chronic obstructive pulmonary disease (COPD) exacerbation is challenging due to similarities in clinical symptoms. The aim of this study was to evaluate predictors of PE and to derive and validate a COPD-specific PE diagnostic strategy. Methods: A post-hoc analysis of the PEP trial, a prospective multicenter study of patients with COPD hospitalized with acutely worsening respiratory symptoms, was conducted. The outcome predicted was PE at admission. Univariable and multivariable analyses were conducted to evaluate predictors of PE. Receiver operating characteristic curves were computed to determine the most discriminant D-dimer cut-offs. The COPD-specific PE diagnostic strategy was externally validated in the independent SLICE trial cohort. Results: A total of 734 patients were included. At admission, the prevalence of PE and/or proximal deep venous thrombosis (DVT) was 6.5% (95%CI 5.0-8.6%). A COPD-specific PE diagnostic strategy consisting of a 3-item score (type of COPD exacerbation, alternative diagnosis less likely than PE, and clinical signs of DVT) combined with D-dimer at specific cut-offs (1,000 μg/L if 0 score item and 500 μg/L if 1 or 2 score items) was derived. The overall diagnostic failure rate was 0.9% (95%CI 0.4-1.9%) and 392 patients (53.4%) would need imaging to rule out PE. The external validation showed comparable results. Conclusion: A COPD-specific PE diagnostic strategy was derived specifically for patients with COPD and acutely worsening respiratory symptoms. Further prospective validation of this diagnostic algorithm is needed prior to integrating it in clinical practice.
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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 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".