Does clinical trial participation improve outcomes in patients with ovarian cancer?
Bibliographic record
Abstract
INTRODUCTION: Treatment on a clinical trial is considered to be beneficial to oncology patients. However, supportive evidence for this is scarce. Trial effect describes the phenomenon of improved health outcomes in patients treated with standard of care (SOC) on trial compared to those receiving SOC outside of a clinical trial. We evaluated trial effect in patients with ovarian cancer treated at our tertiary cancer centre. METHODS: We performed a retrospective cohort study of patients with ovarian cancer treated at The Christie National Health Service Foundation Trust. Patients treated on one of three first-line clinical trials: (SCOTROC-4, ICON-5, ICON-7) were matched (for age, International Federation of Gynaecology and Obstetrics stage, surgical status and performance status) with individuals receiving the same SOC off trial. Survival was calculated using Kaplan-Meier methodology. RESULTS: 60 patients were evaluated; 30 on trial and 30 on SOC off trial. The median progression-free survival (PFS) was 21.8 months (control group) and 25.9 months (trial group), median overall survival (OS) was 64.3 months (control group) and 68.9 months (trial group). There was no difference in PFS (log-rank test: HR 0.87 (95% CI 0.48 to 1.54), p=0.6) or OS (log-rank test: HR 0.87 (95% CI 0.46 to 1.64), p=0.7) between groups. CONCLUSIONS: Patient survival was similar regardless if treated on trial or as SOC. Our findings do not support trial effect, at least in a tertiary cancer centre. Clinical trial participation in specialised cancer centres promotes best practice to the benefit of all patients. These findings may impact discussions round consent of patients to trials and organisation of oncology services.
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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 | 0.073 | 0.214 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".