Aetiology, diagnosis and treatment of moderate-to-severe haemoptysis in a North American academic centre
Bibliographic record
Abstract
Significant haemoptysis is a frightening event for patients and clinicians alike. There is a paucity of contemporary literature on the subject. A retrospective analysis of hospitalisations for haemoptysis of more than 50 mL·day−1in a tertiary referral centre during a 5-year period was performed. Patient's characteristics, haemoptysis aetiology, management and outcome were individually recorded. The aim of this study was to detail the causes of moderate (50–200 mL·day−1) to severe (>200 mL·day−1) haemoptysis along with the diagnostic measures and treatment options used in their management in a 21st century, tertiary-care North American centre. A total of 165 hospitalisations for moderate-to-severe haemoptysis were included in the analysis. Lung cancer (30.3%) and bronchiectasis (27.9%) proved to be most frequent aetiologies. Computed tomography (CT) imaging and bronchoscopy were complementary in identifying the source of bleeding. Bronchial artery embolisation (BAE) was the most common treatment approach (61.8%) and resulted in initial bleeding control in 73.5% of cases. In-hospital mortality was 13.9%, varying from 3.3% in the moderate group to 24.7% in the severe group. Despite being the favoured approach in patients with more severe bleeding, initial BAE therapy was associated with a trend towards lower mortality compared to initial non-BAE therapy. In summary, lung cancer and bronchiectasis were the main causes of moderate-to-severe haemoptysis in our population, CT and bronchoscopy are complementary in identifying the source of bleeding, bleeding volume is associated with outcomes and BAE is a key management tool.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".