Clinical benefit and fragility evaluation of systemic therapy trials for advanced soft tissue sarcoma
Bibliographic record
Abstract
BACKGROUND: The clinical benefit of systemic anticancer therapies can be unclear despite positive trials, and outcomes may not translate to real-world practice. This study evaluated the benefit of soft tissue sarcoma (STS) treatments using the European Society of Medical Oncology Magnitude of Clinical Benefit Scale (MCBS) v1.1 and measured the robustness of STS trial results using Fragility Index (FI). METHODS: Database searches for adult phase II or III trials in advanced STS (January 1998-December 2023) were performed. Therapies with trial outcomes that met the criteria for MCBS were scored 1-5 (≥4 represents substantial clinical benefit). For randomized clinical trials with positive time-to-event endpoints, the number of additional events that would render results nonsignificant, FI, was calculated and expressed as a proportion of the experimental arm size (fragility quotient [FQ]). Higher FI/FQ implies more robust results. RESULTS: Among 194 trials, 19 (9.8%) were phase III. Most phase II trials (146/175; 83.4%) had single-arm or non-comparative design. Trials that were eligible for MCBS scoring (n = 78; 40.2%) evaluated 56 different agents/regimens. Median MCBS score was 2. Only three agents/regimens (all cytotoxic therapies) had an MCBS score ≥4. Among 47 randomized clinical trials, 16 (8 phase II; 8 phase III) trials had positive outcomes. Median FI was 7 (range, 2-52) and 10 trials (62.5%) had an FQ < 10%, with median of 7% (range, 1%-59%). CONCLUSIONS: Most systemic therapies in STS trials did not confer substantial clinical benefit per European Society of Medical Oncology-MCBS. Additionally, positive randomized trials were often fragile. Novel STS therapy trials should use clinically meaningful endpoints and real-world efficacy confirmation is essential, especially for less robust trials.
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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 | 0.128 | 0.186 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".