Predictive Factors of Clinical Success of Therapeutic Bronchoscopy in Malignant Central Airway Obstruction: Results from the EpiGETIF Registry
Bibliographic record
Abstract
INTRODUCTION: Therapeutic bronchoscopy (TB) is considered a safe and effective treatment for patients with malignant central airway obstruction (MCAO). While many factors have been associated with technical success, it does not always translate in clinical success. Few factors to predict clinical response have been described. The objective of this study was to determine predictive factors of clinical success for patients with MCAO undergoing TB. METHODS: We used the multicenter prospective registry EpiGETIF to collect data from patients with MCAO undergoing TB from January 2019 to June 2021. The criterion for clinical success was dyspnea measured on the Borg scale. Patients were classified as super responders if they had an improvement of 4 points after the procedure. Uni- and multivariate analyses were performed to highlight an association between preprocedural features and clinical success. RESULTS: A total of 496 patients from 24 centers met inclusion criteria. The mean preprocedural Borg score was 6.5 ± 2.0 versus 2.2 ± 1.7 postprocedural (mean difference 4.3 ± 2.3). Overall, 302 patients (60.9%) were considered super responders. The only factor associated with super responders in multivariate analysis was a higher baseline Borg score. The only factor associated with non-super responders was a poor performance status and mechanical ventilation. CONCLUSION: Patients show good clinical results following TB for MCAO, influenced positively by a worse pre-procedure dyspnea and negatively by a worse performance status. No other data could help predict the effectiveness of TB, confirming the complexity of the process and heterogeneity of the target population.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| 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".