Canadian Consensus Recommendations on the Management of Extensive-Stage Small-Cell Lung Cancer
Bibliographic record
Abstract
Small-cell lung cancer (SCLC) is an aggressive, neuroendocrine tumour with high relapse rates, and significant morbidity and mortality. Apart from advances in radiation therapy, progress in the systemic treatment of SCLC had been stagnant for over three decades despite multiple attempts to develop alternative therapeutic options that could improve responses and survival. Recent promising developments in first-line and subsequent therapeutic approaches prompted a Canadian Expert Panel to convene to review evidence, discuss practice patterns, and reach a consensus on the treatment of extensive-stage SCLC (ES-SCLC). The literature search included guidelines, systematic reviews, and randomized controlled trials. Regular meetings were held from September 2022 to March 2023 to discuss the available evidence to propose and agree upon specific recommendations. The panel addressed biomarkers and histological features that distinguish SCLC from non-SCLC and other neuroendocrine tumours. Evidence for initial and subsequent systemic therapies was reviewed with consideration for patient performance status, comorbidities, and the involvement and function of other organs. The resulting consensus recommendations herein will help clarify evidence-based management of ES-SCLC in routine practice, help clinician decision-making, and facilitate the best patient outcomes.
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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 | 0.029 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.010 | 0.004 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".