European Respiratory Society guideline on various aspects of quality in lung cancer care
Bibliographic record
Abstract
This European Respiratory Society guideline is dedicated to the provision of good quality recommendations in lung cancer care. All the clinical recommendations contained were based on a comprehensive systematic review and evidence syntheses based on eight PICO (Patients, Intervention, Comparison, Outcomes) questions. The evidence was appraised in compliance with the GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach. Evidence profiles and the GRADE Evidence to Decision frameworks were used to summarise results and to make the decision-making process transparent. A multidisciplinary Task Force panel of lung cancer experts formulated and consented the clinical recommendations following thorough discussions of the systematic review results. In particular, we have made recommendations relating to the following quality improvement measures deemed applicable to routine lung cancer care: 1) avoidance of delay in the diagnostic and therapeutic period, 2) integration of multidisciplinary teams and multidisciplinary consultations, 3) implementation of and adherence to lung cancer guidelines, 4) benefit of higher institutional/individual volume and advanced specialisation in lung cancer surgery and other procedures, 5) need for pathological confirmation of lesions in patients with pulmonary lesions and suspected lung cancer, and histological subtyping and molecular characterisation for actionable targets or response to treatment of confirmed lung cancers, 6) added value of early integration of palliative care teams or specialists, 7) advantage of integrating specific quality improvement measures, and 8) benefit of using patient decision tools. These recommendations should be reconsidered and updated, as appropriate, as new evidence becomes available.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.060 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".