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Head-to-head comparison of first-line FOLFIRINOX versus gemcitabine plus nabpaclitaxel (GN) in advanced pancreatic cancer (APC): A target trial emulation using Canadian real-world data.

2021· article· en· W3172389519 on OpenAlexaffabout
Devon J. Boyne, Darren R. Brenner, Alind Gupta, E. V. Mackay, Paul Arora, Radek Wasiak, Winson Y. Cheung, Miguel A. Hernán

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineFOLFIRINOXPancreatic cancerObservational studyHazard ratioRandomized controlled trialGemcitabineInternal medicineOncologyClinical trialCancerIrinotecanConfidence interval

Abstract

fetched live from OpenAlex

e18713 Background: When randomized trials are not available, observational real-world data can be used to emulate a (hypothetical) target trial. The procedure starts with the specification of the protocol of the target trial, whose components are then explicitly emulated using observational data. This approach prevents biases that are common when using more conventional methods for real-world data. Advanced pancreatic cancer represents an opportunity for trial emulation since two main frontline therapies, FOLFIRINOX and GN, have never been directly compared in a randomized fashion. The choice between the two regimens is largely based on physician discretion and patient preference rather than direct comparison of effectiveness. Methods: We emulated a target trial using linked data from the provincial cancer registry, electronic health records and various administrative databases from Alberta, Canada. Eligible individuals had locally advanced or metastatic pancreatic cancer diagnosed between Jan. 2015- Dec. 2018, no prior treatment, and adequate hematologic and serum creatinine values. They were followed from diagnosis until March 2020, death, or date of last known contact with the healthcare system. We estimated the effect of initiating FOLFIRINOX vs. GN within 8 weeks of diagnosis on overall survival. Cloning, artificial censoring, and inverse probability weighting were used to address unknown treatment assignment at baseline, non-adherence, and confounding. Adjusted Kaplan-Meier survival curves and hazard ratios were estimated. Results: Of 298 eligible individuals, 70 adhered to the FOLFIRINOX strategy and 147 to the GN strategy. The mean age was 65 years, 173 (58%) were male, and 247 (83%) had metastatic disease. The adjusted median survival, 1-year survival, and 2-year survival for FOLFIRNOX was 8.2 months (95% CI: 5.3 to 9.4), 36.9% (22.2 to 55.8), and 14.1% (4.8 to 32.2), respectively; and for GN was 4.8 months (3.3 to 5.3), 22.2% (13.6 to 35.9), and 4.7% (1.7 to 13.0), respectively. The adjusted difference in median survival was 3.4 months (0.6 to 11.1) and the adjusted hazard ratio was 0.79 (0.56 to 1.05). Conclusions: Target trial emulations can help to inform medical decision making in situations where head-to-head randomized trial data are unavailable or unfeasible. Findings from this real-world trial emulation suggest improved overall survival with FOLFIRINOX over GN.

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 imitation

Not 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.

metaresearch head score (Codex)0.039
metaresearch head score (Gemma)0.050
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.666
Threshold uncertainty score0.664

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.050
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0020.005
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.474
GPT teacher head0.610
Teacher spread0.136 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes2
Has abstractyes

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