Real‐world outcomes of FOLFIRINOX vs gemcitabine and nab‐paclitaxel in advanced pancreatic cancer: A population‐based propensity score‐weighted analysis
Bibliographic record
Abstract
BACKGROUND: In Ontario, FOLFIRINOX (FFX) and gemcitabine + nab-paclitaxel (GnP) have been publicly funded for first-line unresectable locally advanced pancreatic cancer (uLAPC) or metastatic pancreatic cancer (mPC) since April 2015. We examined the real-world effectiveness and safety of FFX vs GnP for advanced pancreatic cancer, and in uLAPC and mPC. METHODS: Patients receiving first-line FFX or GnP from April 2015 to March 2017 were identified in the New Drug Funding Program database. Baseline characteristics and outcomes were obtained through the Ontario Cancer Registry and other population-based databases. Overall survival (OS) was assessed using Kaplan-Meier and weighted Cox proportional hazard models, weighted by the inverse propensity score adjusting for baseline characteristics. Weighted odds ratio (OR) for hospitalization and emergency department visits (EDV) were estimated from weighted logistic regression models. RESULTS: For 1130 patients (632 FFX, 498 GnP), crude median OS was 9.6 and 6.1 months for FFX and GnP, respectively. Weighted OS was improved for FFX vs GnP (HR = 0.77, 0.70-0.85). Less frequent EDV and hospitalization were observed in FFX (EDV: 67.8%; Hospitalization: 49.2%) than GnP (EDV: 77.7%; Hospitalization: 59.3%). More frequent febrile neutropenia-related hospitalization was observed in FFX (5.8%) than GnP (3.3%). Risk of EDV and hospitalization were significantly lower for FFX vs GnP (EDV: OR = 0.68, P = .0001; Hospitalization: OR = 0.76, P = .002), whereas the risk of febrile neutropenia-related hospitalization was significantly higher (OR = 2.12, P = .001). Outcomes for uLAPC and mPC were similar. CONCLUSION: In the real world, FFX had longer OS, less frequent all-cause EDV and all-cause hospitalization, but more febrile neutropenia-related hospitalization compared to GnP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".