Analysis of Heterogeneity in Survival Benefit of Immunotherapy in Oncology According to Patient Demographics and Performance Status
Bibliographic record
Abstract
OBJECTIVES: Immunotherapy (IO) has become standard of care (SOC) for many advanced malignancies, although identifying patients likely to benefit remains difficult. We sought to assess whether demographic factors are associated with response to IO, compared with SOC systemic therapy, using stratified meta-analysis. METHODS: A systematic review of MEDLINE, PubMed, Embase, and Scopus from inception to October 2, 2018. Randomized controlled trials comparing IO to SOC in patients with advanced solid organ malignancies were included if results were stratified by age, performance status (PS), or race, assessing overall survival (OS). Pooled hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated for each group using random-effects models independently. RESULTS: We identified 21 eligible randomized controlled trials, including 20 stratified by age, 17 by PS, and 4 by race. Patients with PS 0 (HR, 0.74; 95% CI, 0.63-0.86) and PS≥1 (HR, 0.75; 95% CI, 0.68-0.83) had similar OS benefits from IO compared with SOC (P=0.80). There was no difference on the basis of patient race (white vs. nonwhite) (P=0.46). IO demonstrated an OS benefit for younger (below 65 y: HR, 0.73; 95% CI, 0.65-0.82) and older (65 y and above: HR, 0.79; 95% CI, 0.71-0.88) patients with no difference between age groups (P=0.27). Among prespecified subgroup analyses, there was significant effect modification in 2 subgroups: younger patients in the first-line setting (P=0.03) and those receiving anti-CTLA-4 drugs (P=0.05). CONCLUSIONS: When examining OS using stratified meta-analysis, we did not demonstrate significant differences in IO efficacy according to patient age, PS or race, though data on race were sparse.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".