Morbidity and mortality following surgical lung biopsy in interstitial lung disease in a specialized multidisciplinary center
Bibliographic record
Abstract
Objective: This study aims to assess the mortality and complications of surgical lung biopsy (SLB) for patients with interstitial lung disease (ILD) in a high volume centre with an experienced multidisciplinary ILD team. Methods: The study population consisted of a cohort of patients who underwent SLB for the presumed diagnosis of ILD between June 2009 to November 2019. In-hospital mortality and 90-day mortality were the primary endpoints. Patient and surgical covariates were tested to verify their association with 90-day mortality using multivariate logistic regression analyses. Results: Of the 216 consecutive operations, 35 (16.2%) were performed as a non-elective procedure and videothoracoscopy was utilized in 94.5% of the cases (n=205). The in-hospital mortality and 90-day mortality were 3.7% (n=8) and 4.2% (n=9), respectively. Open thoracotomy [OR 0.012 95% CI (0.01 - 0.001)] and postoperative complications [OR 0.015 95% CI (0.02 – 0.001)] were significantly associated with 90-day mortality (table1). The odds ratio for death at 90 days post-surgery was 12.2 [95% CI: 2.9 – 51(p<0.001)] in non-elective cases and 0.77 (95% CI :0.18-3.2) for elective surgery after decision and referral from the multidisciplinary team. Conclusions: The mortality after SLB for patients with ILD is high even in a specialized referral center, specifically in non-elective interventions. These outcomes underscore the risk of emergent SLB and the importance of case triage with input from the multidisciplinary team.
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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 | 0.000 |
| 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) | 0.002 | 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".