Outcomes of FOLFIRINOX (FFX) and gemcitabine+nab-paclitaxel (GnP) in initially unresectable locally advanced pancreatic cancer (uLAPC): A population-based study.
Bibliographic record
Abstract
394 Background: Data regarding the benefits of FFX and GnP in patients (pts) with initially uLAPC is limited. FFX and GnP have been universally publicly funded for first-line uLAPC in Ontario, Canada, since April 2015. The aims of this study are to determine (1) the overall survival (OS) of pts receiving FFX and GnP, (2) the surgical conversion rate of FFX and GnP, and (3) whether resection is associated with better OS in pts with uLAPC in an unselected, real world population. Methods: All pts in Ontario who started first-line FFX, GnP or gemcitabine (G) for uLAPC between April 2015 and March 2016 were identified in Cancer Care Ontario’s New Drug Funding Program database. They were linked to the Ontario Cancer Registry and other population-based databases to ascertain baseline characteristics (age, sex, performance status (PS), locating of tumor, income quintile, and rural residence) and outcomes (pancreatic cancer resection and vital status). Crude and adjusted models of OS were generated using Kaplan-Meier the method and Cox regression. Surgical resection was modelled as a time-dependent variable to examine its association with OS. Results: We identified 147 pts with uLAPC (mean age = 65, 44% female, 31% ECOG PS 0, 61% PS 1, 60% pancreatic head). Ninety (61.2%), 40 (27.2%) and 17 (11.6%) patients were treated with FFX, GnP and G, respectively. With a median follow-up of 7.5 months, median OS was not reached. The 6-month OS rate was 87.8%, 75.1% and 76.4% for FFX, GnP and G, respectively (p = 0.33). Resection occurred in 12 (8.2%) patients, with 10 (11.1%) and 2 (5.0%) treated with FFX and GnP, respectively ( p= 0.34). Surgical resection after initial chemotherapy was not associated with better OS in multivariable analysis (HR 0.26, 95%CI 0.03-1.98, p= 0.19). Conclusions: Pts with uLAPC treated with FFX and GnP appeared to have a reasonable OS in the real world, with > 75% of pts alive at 6 months. Surgical conversion rate in this unselected population appeared to be less than other single institutional studies. The current findings do not appear to show an early surgical benefit, but longer follow-up will be required to assess the potential long-term benefit of surgery.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".