A Delphi Consensus report from the "Prolonged Air Leak: A Survey" study group on prevention and management of postoperative air leaks after minimally invasive anatomical resections
Bibliographic record
Abstract
OBJECTIVES: This study reports the results of an international expert consensus process evaluating the assessment of intraoperative air leaks (IAL) and treatment of postoperative prolonged air leaks (PAL) utilizing a Delphi process, with the aim of helping standardization and improving practice. METHODS: A panel of 45 questions was developed and submitted to an international working group of experts in minimally invasive lung cancer surgery. Modified Delphi methodology was used to review responses, including 3 rounds of voting. The consensus was defined a priori as >50% agreement among the experts. Clinical practice standards were graded as recommended or highly recommended if 50-74% or >75% of the experts reached an agreement, respectively. RESULTS: A total of 32 experts from 18 countries completed the questionnaires in all 3 rounds. Respondents agreed that PAL are defined as >5 days and that current risk models are rarely used. The consensus was reached in 33/45 issues (73.3%). IAL were classified as mild (<100 ml/min; 81%), moderate (100-400 ml/min; 71%) and severe (>400 ml/min; 74%). If mild IAL are detected, 68% do not treat; if moderate, consensus was not; if severe, 90% favoured treatment. CONCLUSIONS: This expert consensus working group reached an agreement on the majority of issues regarding the detection and management of IAL and PAL. In the absence of prospective, randomized evidence supporting most of these clinical decisions, this document may serve as a guideline to reduce practice variation.
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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 | 0.116 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".