Real-world outcomes among patients (pts) treated with gemcitabine (GEM)-based therapy post-FOLFIRINOX (FFOX) failure in advanced pancreatic cancer (APC).
Bibliographic record
Abstract
303 Background: Limited evidence exists for the selection of chemotherapy in APC after first-line (1stL) FFOX. Gemcitabine/nab-paclitaxel (GEMNAB) is publicly funded for second-line (2ndL) use in the provinces of Alberta (AB) and Manitoba (MB), but is not covered in British Columbia (BC). We compared population-based outcomes by region to examine the utility of 2ndL GEMNAB versus GEM alone. Methods: We identified pts treated with 1stL FFOX between 2013-2015 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, and compared with log-rank test. Results: 370 pts treated with 1stL FFOX were identified (145 AB/MB, 225 BC), with a median age of 61y, 42% female, and 68% with metastatic disease (similar in both groups). Receipt of 2ndL therapy was 49% AB/MB vs 44% BC ( p = 0.35), and time from diagnosis to 2ndL therapy measured 7.6 mos AB/MB versus 9.4 mos BC ( p = 0.1). The distribution of 2ndL gemcitabine use was: 72% GEMNAB, 23% GEM in AB/MB versus 27% GEMNAB, 66% GEM in BC ( p < 0.001). Median overall survival (OS) from diagnosis was similar: 12.4 mos in AB/MB versus 10.9 mos in BC ( p = 0.75). On Cox regression analysis, region was not significant. A secondary survival analysis by 2ndL regimen demonstrated a median OS of 18.0 mos with GEMNAB versus 14.3 mos GEM ( p < 0.01). Conclusions: In our population-based comparison of APC pts treated with 1stL FFOX, survival outcomes were comparable regardless of publicly funded access to 2ndL GEMNAB versus GEM. OS by regimen favored 2ndL GEMNAB, but patient selection may be largely responsible for this difference. Randomized trials are needed to demonstrate the benefit of GEMNAB post-FFOX in APC.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".