A Bayesian Meta-Analysis of Multiple Treatment Comparisons of Systemic Regimens for Advanced Pancreatic Cancer
Bibliographic record
Abstract
BACKGROUND: For advanced pancreatic cancer, many regimens have been compared with gemcitabine (G) as the standard arm in randomized controlled trials. Few regimens have been directly compared with each other in randomized controlled trials and the relative efficacy and safety among them remains unclear. METHODS: A systematic review was performed through MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and ASCO meeting abstracts up to May 2013 to identify randomized controlled trials that included advanced pancreatic cancer comparing the following regimens: G, G+5-fluorouracil, G+ capecitabine, G+S1, G+ cisplatin, G+ oxaliplatin, G+ erlotinib, G+ nab-paclitaxel, and FOLFIRINOX. Overall survival and progression-free survival with 95% credible regions were extracted using the Parmar method. A Bayesian multiple treatment comparisons was performed to compare all regimens simultaneously. RESULTS: Twenty-two studies were identified and 16 were included in the meta-analysis. Median overall survival, progression free survival, and response rates for G arms from all trials were similar, suggesting no significant clinical heterogeneity. For overall survival, the mixed treatment comparisons found that the probability that FOLFIRINOX was the best regimen was 83%, while it was 11% for G+ nab-paclitaxel and 3% for G+ S1 and G+ erlotinib, respectively. The overall survival hazard ratio for FOLFIRINOX versus G+ nab-paclitaxel was 0.79 [0.50-1.24], with no obvious difference in toxicities. The hazard ratios from direct pairwise comparisons were consistent with the mixed treatment comparisons results. CONCLUSIONS: FOLFIRINOX appeared to be the best regimen for advanced pancreatic cancer probabilistically, with a trend towards improvement in survival when compared with other regimens by indirect comparisons.
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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 | 0.115 | 0.183 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.018 | 0.072 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| 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".