Association between control group therapy and magnitude of clinical benefit of cancer drugs.
Bibliographic record
Abstract
Abstract Background: Little is known about the impact of control group therapy on clinical benefit scales such as American Society of Clinical Oncology Value Framework (ASCO-VF), European Society for Medical Oncology Magnitude Clinical Benefit Scale (ESMO-MCBS), National Comprehensive Cancer Network (NCCN) Evidence Blocks and ASCO Cancer Research Committee (ASCO-CRC). Methods: We searched Drugs@FDA to identify cancer drugs approved between January 2012 and December 2021 based on randomized trials (RCTs). Definition of substantial clinical benefit was based on recommendations for each scale. Associations between characteristics of control group therapy and clinical benefit were explored using logistic regression. Results: RCTs with a control group of active treatment plus placebo were associated with significantly lower odds of substantial benefit with ESMO-MCBS (OR:0.27, P=.003) and ASCO-VF (OR:0.30, P=.008) but not with NCCN Evidence Blocks or ASCO-CRC. This effect was attenuated and lost statistical significance without adjustment for quality of life (QoL) and/or toxicity (ESMO-MCBS OR:0.50, P=.17; ASCO-VF OR:0.49, P=.11). Discussion: Clinical benefit scales can be sensitive to control group therapy. RCTs with substantial overlap between experimental and control therapy showed lower magnitude of clinical benefit using ESMO-MCBS and ASCO-VF scales; possibly due to differences in the weighting of QoL and toxicity between different frameworks.
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 | 0.120 | 0.287 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".