Reliability of Oncology Value Framework Outputs: Concordance Between Independent Research Groups
Bibliographic record
Abstract
Research groups are increasingly utilizing value frameworks, but little is known of their reliability. To assess framework concordance and interrater reliability between two major value frameworks currently in use, we identified all previously published datasets containing both scores from the American Society of Clinical Oncology Value Framework (ASCO-VF) and grades from the European Society for Medical Oncology-Magnitude of Clinical Benefit Scale (ESMO-MCBS). The intraclass correlation coefficient (ICC) was used to assess interrater reliability. Four eligible studies contained drugs evaluated by both value frameworks, resulting in a dataset of 39 grades/scores for discrete drug indications. ICC was 0.82 (95% confidence interval = 0.70 to 0.90) for ASCO-VF and 0.88 (95% confidence interval = 0.80 to 0.93) for ESMO-MCBS. Absolute concordance was found to be 5% for ASCO-VF and 44% for ESMO-MCBS, increasing to 74% and 80% when deviations within 20 points and 1 grade were considered, respectively. Interrater reliability of ASCO-VF and ESMO-MCBS is, therefore, near perfect, while absolute concordance is poor. This has implications when considering framework outputs in drug funding or treatment decision making.
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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 | 0.399 | 0.648 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".