Methodological and reporting standards for quality-of-life data eligible for European Society for Medical Oncology-Magnitude of Clinical Benefit Scale (ESMO-MCBS) credit
Bibliographic record
Abstract
BACKGROUND: The European Society for Medical Oncology-Magnitude of Clinical Benefit Scale (ESMO-MCBS) has been developed to grade clinical benefit of cancer therapies. Improvement in quality of life (QoL) is considered relevant, especially in the non-curative setting. This is reflected by an upgrade of the preliminary ESMO-MCBS score if QoL is improved compared to the control arm or a downgrade if an improvement in progression-free survival is not paralleled by an improvement in QoL or overall survival. Given the importance of QoL for the final score, a need to ensure the robustness of QoL data was recognised. DESIGN: A checklist was created based on existing guidelines for QoL research. Field testing was carried out using clinical trials that either received an adjustment of the preliminary ESMO-MCBS score based on QoL or had QoL as the primary endpoint. Several rounds of revision and re-testing of the checklist were undertaken until a final consensus was reached. RESULTS: The final checklist consists of four items and can be applied if three prerequisites are met: (i) QoL is at least a secondary endpoint, (ii) evidence of reliability and validity of the instrument is provided, and (iii) a statistically and clinically significant improvement in QoL is observed. The four items on the checklist pertain to the (i) hypothesis, (ii) compliance and missing data, (iii) presentation of the results, and (iv) statistical and clinical relevance. Field testing revealed that a clear QoL hypothesis and correction for multiple testing were mostly lacking, while the main statistical method was always described. CONCLUSIONS: Implementation of the ESMO-MCBS QoL checklist will facilitate objective and transparent decision making on QoL data within the ESMO-MCBS scoring process. Trials published until 1 January 2025 will have to meet the prerequisites and at least two items for crediting QoL benefit in the final ESMO-MCBS score. Trials published thereafter will have to meet all four items.
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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 | 0.645 | 0.830 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.019 |
| Bibliometrics | 0.031 | 0.025 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.010 | 0.011 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.026 | 0.009 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".