European Respiratory Society and European Society of Thoracic Surgeons clinical practice guideline on fitness for curative intent treatment of lung cancer
Bibliographic record
Abstract
A multidisciplinary panel of lung cancer experts with a special interest in functional evaluation of lung cancer patients, and lung cancer patient representatives, has been facilitated by the European Society of Thoracic Surgeons and the European Respiratory Society to provide healthcare professionals with practical and up-to-date recommendations for the assessment of patients' fitness for curative intent treatments for lung cancer. The panel formulated four PICO (population, intervention, comparison and outcomes) questions and seven complementary narrative questions. Both types of questions were assigned to groups of at least two experts. A medical librarian conducted the literature searches, and the authors selected relevant studies based on predefined inclusion criteria. Risk of bias was assessed using the QUIPS (Quality in Prognosis Studies) tool. Data were summarised and the certainty of evidence was assessed with GRADE (Grading of Recommendations, Assessment, Development and Evaluations) and the Evidence to Decisions framework was used to formulate recommendations. A series of multidisciplinary recommendations was formulated about the utilisation of pulmonary function tests, split lung function values, exercise tests, cardiologic testing, and the role of prehabilitation, sublobar resections, risk scores and comorbidities in selecting patients for curative intent treatment.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".