Impact of Value Frameworks on the Magnitude of Clinical Benefit: Evaluating a Decade of Randomized Trials for Systemic Therapy in Solid Malignancies
Bibliographic record
Abstract
In the era of rapid development of new, expensive cancer therapies, value frameworks have been developed to quantify clinical benefit (CB). We assessed the evolution of CB since the 2015 introduction of The American Society of Clinical Oncology and The European Society of Medical Oncology value frameworks. Randomized clinical trials (RCTs) assessing systemic therapies for solid malignancies from 2010 to 2020 were evaluated and CB (Δ) in 2010–2014 (pre-value frameworks (PRE)) were compared to 2015–2020 (POST) for overall survival (OS), progression-free survival (PFS), response rate (RR), and quality of life (QoL). In the 485 studies analyzed (12% PRE and 88% POST), the most common primary endpoint was PFS (49%), followed by OS (20%), RR (12%), and QoL (6%), with a significant increase in OS and decrease in RR as primary endpoints in the POST era (p = 0.011). Multivariable analyses revealed significant improvement in ΔOS POST (OR 2.86, 95% CI 0.46 to 5.26, p = 0.02) while controlling for other variables. After the development of value frameworks, median ΔOS improved minimally. The impact of value frameworks has yet to be fully realized in RCTs. Efforts to include endpoints shown to impact value, such as QoL, into clinical trials are warranted.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.364 | 0.477 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".