Real-world Outcomes Among Patients Treated With Gemcitabine-based Therapy Post-FOLFIRINOX Failure in Advanced Pancreatic Cancer
Bibliographic record
Abstract
OBJECTIVES: Limited evidence exists for chemotherapy selection in advanced pancreatic cancer (APC) after first-line FOLFIRINOX. Second-line gemcitabine/nab-paclitaxel (GEMNAB) is publicly funded in the Canadian provinces of Alberta (AB) and Manitoba (MB), but not in British Columbia (BC). We compared population-based outcomes by region to examine the utility of second-line GEMNAB versus gemcitabine (GEM) alone. METHODS: We identified patients treated with first-line FOLFIRINOX between 2013 and 2015 across BC, AB, and MB. Baseline characteristics and treatment regimens were compared between AB/MB and BC. Survival outcomes were assessed by the Kaplan-Meier method and compared with log-rank test. RESULTS: A total of 368 patients were treated with first-line FOLFIRINOX (143 AB/MB, 225 BC): median age 61 (interquartile range: 55 to 68) years, 42% comprising female individuals, and 67% with metastatic disease. Receipt of second-line therapy was 48% in AB/MB versus 44% in BC (P=0.35), and time from diagnosis to second-line therapy was 7.7 (AB/MB) versus 9.4 months (BC; P=0.1). Distribution of second-line GEM use: 73% GEMNAB, 23% GEM (AB/MB) versus 27% GEMNAB, 66% GEM (BC; P<0.001). Median overall survival (OS) from diagnosis was similar: 12.4 (AB/MB) versus 11.5 months (BC; P=0.91). On Cox regression analysis, region was not significant. Secondary survival analysis by second-line regimen demonstrated a median OS of 18.0 months with GEMNAB versus 14.3 months with GEM (P<0.01). CONCLUSIONS: In this population-based comparison of APC patients treated with first-line FOLFIRINOX, survival outcomes were comparable regardless of funded access to second-line GEMNAB. OS by regimen favored second-line GEMNAB, but patient selection may be largely responsible for this difference.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".