Integrating Systematic Reviews into Supportive Care Trial Design: The Rethinking Clinical Trials (REaCT) Program
Bibliographic record
Abstract
Purpose: To review the successes and challenges of integrating systematic reviews (SRs) into the Rethinking Clinical Trials (REaCT) Program. Methods: All REaCT program SRs were evaluated and descriptive summaries presented. Results: Twenty-two SRs have been performed evaluating standard of care interventions for the management of: breast cancer (n = 15), all tumour sites (n = 4), breast and prostate cancers (n = 2), and prostate cancer (n = 1). The majority of SRs were related to supportive care (n = 14) and survivorship (n = 5) interventions and most (19/22, 86%) confirmed the existence of uncertainty relating to the clinical question addressed in the SR. Most SRs (15/22, 68%) provided specific recommendations for future studies and results were incorporated into peer-reviewed grant applications (n = 6) and clinical trial design (n = 12). In 12/22 of the SRs, the first author was a trainee. All SRs followed PRISMA guidelines. Conclusion: SRs are important for identifying and confirming clinical equipoise and designing trials. SRs provide an excellent opportunity for trainees to participate in research.
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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 | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | 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 | 0.880 | 0.920 |
| Meta-epidemiology (narrow) | 0.006 | 0.008 |
| Meta-epidemiology (broad) | 0.012 | 0.017 |
| Bibliometrics | 0.025 | 0.024 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.012 | 0.025 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".