Do patients with reduced or excellent performance status derive the same clinical benefit from novel systemic cancer therapies? A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Whether patients with excellent and reduced performance status (PS) derive different net clinical benefit from novel anticancer systemic therapies on clinical trials is unclear. MATERIALS AND METHODS: A systematic review was conducted of randomised controlled trials (RCTs) cited for drug approvals between 2006 and August 2015 by the Food and Drug Administration, the European Medicines Agency and Health Canada. Included studies had overall survival (OS) and/or progression-free survival (PFS) primary endpoints. Meta-analyses of OS/PFS based on PS dichotomised into excellent and reduced subgroups were performed using random effects. RESULTS: The systematic review identified 110 RCTs, with none reporting PS subgroup analyses for toxicity and 66 (60%) for efficacy. For these 66 RCTs involving 44 511 patients, pooled HRs for excellent and reduced groups were 0.65 (95% CI 0.61 to 0.70) and 0.67 (95% CI 0.62 to 0.72), respectively, with no difference between the two groups (p=0.68). Sensitivity analyses based on drug or cancer type and type of endpoints (OS or PFS) demonstrated similar results. CONCLUSIONS: efficacy from novel systemic therapy was found for patients with reduced PS when compared with patients with excellent PS for the range which were included in modern RCTs. Reporting of PS subgroup analyses of toxicities and more inclusion of patients with borderline low PS in RCTs should be considered for a more comprehensive understanding of the net clinical benefits of contemporary systemic therapies in patients across the spectrum of different PS.
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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 | 0.030 | 0.068 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.042 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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, 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".