Major bleeding in patients with acute pulmonary embolism: results from the COPE study
Bibliographic record
Abstract
Abstract Background In patients with acute pulmonary embolism (PE), major bleeding (MB) is the counterbalance for anticoagulant and thrombolytic treatment. In patients with acute, symptomatic PE from a prospective, multicentre study (COPE), we aimed to assess independent predictors, evaluate the performance of currently available scores and identify phenotypic clusters for prediction of MB. Methods The primary outcome was MB during 30-day follow-up. MB was defined according to ISTH. MBs were classified as early (<7 days) and late (≥7 days) based on days from PE diagnosis. Results Among 5,213 patients with acute PE, 159 experienced MB (3.1%), 78 as early and 81 as late events. MB were intracranial haemorrhage in 17 (0.3%), fatal in 6 and required transfusion >2 RBC unit and/or Hb drop ≥2g/dl in 138 patients (2.6%). MB occurred in 9.3% and 2.5% of patients receiving and not receiving thrombolysis. Active cancer, previous bleeding, dementia, anemia, syncope and thrombolysis were independent predictors of MB at 30 days and of early MB; only active cancer, previous bleeding and anemia were independent predictors of late MB. The PE-SARD, the BACS and VTE-Bleed scores had better performance in predicting early than late MBs and modest performance in predicting thrombolysis-associated MB. Four phenotypic clusters of patients were identified with different risk for MB; A= old patients with low prevalence of comorbidities, B=old patients with comorbidities and intermediate or high-risk PE, C= middle-aged patients with cancer or and D= young patients with low prevalence of comorbidities. Conclusions In patients with acute PE, risk factors for early and late MB within 30 days differ. Compared to stratification strategies solely based on bleeding predictors, phenotyping can offer better accuracy in identifying patients at risk for MB.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".