Therapeutic bronchoscopy for malignant central airway obstruction: Introduction to the <scp>EpiGETIF</scp> registry
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: EpiGETIF is a web-based, multicentre clinical database created in 2019 aiming for prospective collection of data regarding therapeutic rigid bronchoscopy (TB) for malignant central airway obstruction (MCAO). METHODS: Patients were enrolled into the registry from January 2019 to November 2022. Data were prospectively entered through a web-interface, using standardized definitions for each item. The objective of this first extraction of data was to describe the population and the techniques used among the included centres to target, facilitate and encourage further studies in TB. RESULTS: Overall, 2118 patients from 36 centres were included. Patients were on average 63.7 years old, mostly male and smokers. Most patients had a WHO score ≤2 (70.2%) and 39.6% required preoperative oxygen support, including mechanical ventilation in 6.7%. 62.4% had an already known histologic diagnosis but only 46.3% had received any oncologic treatment. Most tumours were bronchogenic (60.6%), causing mainly intrinsic or mixed obstruction (43.3% and 41.5%, respectively). Mechanical debulking was the most frequent technique (67.3%), while laser (9.8%) and cryo-recanalization (2.7%) use depended on local expertise. Stenting was required in 54.7%, silicone being the main type of stent used (55.3%). 96.3% of procedure results were considered at least partially successful, resulting in a mean 4.1 points decrease on the Borg scale of dyspnoea. Complications were noted in 10.9%. CONCLUSION: This study exposes a high volume of TB that could represent a good source of future studies given the dismal amount of data about the effects of TB in certain populations and situations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".