A Bayesian network meta-analysis of systemic regimens for advanced pancreatic cancer.
Bibliographic record
Abstract
4015 Background: For advanced pancreatic cancer, many regimens have been compared with gemcitabine (G) in randomized control trials (RCTs). Few have been compared with each other directly in RCTs and the relative efficacy and safety among them are unclear. Methods: A systematic review was performed through MEDLINE, EMBASE, Cochrane Central Register of Contorlled Trials and ASCO meeting abstracts up to Jan 2013 to identify RCTs that included metastatic pancreatic cancer comparing the following regimens: G, G+5-flourouracil (GF), G+capecitabine (GCap), G+S1 (GS), G+cisplatin (GCis), G+oxaliplatin (GOx), G+erlotinib (GE), G+Abraxane (GA) and FOLFIRINOX. Studies were reviewed by two authors and discrepancies were resolved by consensus or by a third author. Data including overall survival (OS), progression-free survival (PFS), response rate (RR), and side-effects were extracted. A Bayesian network meta-analysis with random effects was performed using WinBUGS to compare all regimens simultaneously. Results: Twenty-two studies involving 6,252 patients were identified, with 21 RCTs involving G, 4 with GF, 3 with GCap, 2 with GS, 6 with GCis, 3 with GOx, 1 with GE, 1 with GA and 1 with FOLFIRINOX. Median OS, PFS and RR for G arms from all trials were similar, suggesting the absence of significant clinical heterogeneity among RCTs. For OS, the results of the Bayesian network meta-analysis found that the probability that FOLFIRINOX was the best regimen was 71%, while it was 19% for GS, 7% for GA and 2% for GE respectively. The OS hazard ratio (HR) for FOLFIRINOX vs. GS was 0.82 (95% credible region (CR): 0.53-1.35), the OS HR for FOLFIRINOX vs. GA was 0.77 (95% CR: 0.51-1.23), and the OS HR for FOLFIRINOX vs. GE was 0.67 (95% CR: 0.45-1.08). Similar ranking and probabilities were observed for the best regimen for PFS. Conclusions: FOLFIRINOX appeared to be the best regimen for advanced pancreatic cancer probabilistically, with a trend towards improvement in OS and PFS when compared with GS, GA, or GE by indirect comparisons. In the absence of direct pairwise comparisons of these regimens from RCTs, network meta-analysis helps synthesize evidence and inform decision making.
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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 | 0.047 | 0.065 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.044 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".