Risk of recurrent venous thromboembolism and bleeding in patients with acute isolated subsegmental pulmonary embolism
Bibliographic record
Abstract
INTRODUCTION: Approximately 10 % of all diagnosed pulmonary embolism are isolated to the subsegmental vessels. The risk of recurrent venous thromboembolism (VTE) in patients with an acute subsegmental pulmonary embolism (SSPE) managed with or without anticoagulant therapy remains poorly understood. METHODS: This is an observational cohort study including consecutive adult patients diagnosed with acute isolated SSPE between June 01, 2019, and August 31, 2022. We excluded patients with a concomitant diagnosis of deep vein thrombosis and those who had an indication for long-term anticoagulation. The primary outcome was objectively confirmed recurrent VTE. RESULTS: Overall, 118 patients with acute SSPE were included in the analysis. The mean (± standard deviation [SD]) age of the participants was 59 ± 17 years and 44 % of them had active cancer. Mean (±SD) duration of follow-up was 438 ± 426 days. Seventy-seven patients (65 %) were initially treated with anticoagulation, whereas 41 patients (35 %) were not. Of the 77 patients receiving anticoagulant therapy, 23 (30 %) received extended-duration anticoagulation (beyond 3 months) for secondary prevention. Overall, recurrent VTE events occurred in 6/118 (5 %, 95 % CI 2.4 to 10.7) patients. Four events (4/77 = 5.2 %, 95 % CI 2.0 to 12.6) occurred in initially treated patients. Two recurrent VTE occurred in patients initially left untreated (2/41 = 4.9 %, 95 % CI 1.4 to 16.1). Half of the recurrent VTE occurred in patients with active cancer. CONCLUSIONS: Most patients diagnosed with an acute SSPE received anticoagulation. The incidence of recurrent VTE detected over time was relatively high, especially in patients with cancer.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".