Randomised trial of first-line bronchial artery embolisation for non-severe haemoptysis of mild abundance
Bibliographic record
Abstract
BACKGROUND: Whereas first-line bronchial artery embolisation (BAE) is considered standard of care for the management of severe haemoptysis, it is unknown whether this approach is warranted for non-severe haemoptysis. RESEARCH QUESTION: To assess the efficacy on bleeding control and the safety of first-line BAE in non-severe haemoptysis of mild abundance. STUDY DESIGN AND METHODS: This multicentre, randomised controlled open-label trial enrolled adult patients without major comorbid condition and having mild haemoptysis (onset <72 hours, 100-200 mL estimated bleeding amount), related to a systemic arterial mechanism. Patients were randomly assigned (1:1) to BAE associated with medical therapy or to medical therapy alone. RESULTS: Bleeding recurrence at day 30 after randomisation (primary outcome) occurred in 4 (11.8%) of 34 patients in the BAE strategy and 17 (44.7%) of 38 patients in the medical strategy (difference -33%; 95% CI -13.8% to -52.1%, p=0.002). The 90-day bleeding recurrence-free survival rates were 91.2% (95% CI 75.1% to 97.1%) and 60.2% (95% CI 42.9% to 73.8%), respectively (HR=0.19, 95% CI 0.05 to 0.67, p=0.01). No death occurred during follow-up and no bleeding recurrence needed surgery.Four adverse events (one major with systemic emboli) occurred during hospitalisation, all in the BAE strategy (11.8% vs 0%; difference 11.8%, 95% CI 0.9 to 22.6, p=0.045); all eventually resolved. CONCLUSION: In non-severe haemoptysis of mild abundance, BAE associated with medical therapy had a superior efficacy for preventing bleeding recurrences at 30 and 90 days, as compared with medical therapy alone. However, it was associated with a higher rate of adverse events. TRIAL REGISTRATION NUMBER: NCT01278199.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".