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Real-world cost-effectiveness of first-line gemcitabine + nab-paclitaxel versus FOLFIRINOX in patients with advanced pancreatic cancer: A population-based retrospective cohort study in Ontario, Canada.

2022· article· en· W4205731320 on OpenAlexaffabout
Vanessa Sarah Arciero, Jin Luo, Ambika Parmar, Wei Fang Dai, Jaclyn Beca, Michael J. Raphael, Wanrudee Isaranuwatchai, Steven Habbous, Mina Tadrous, Craig C. Earle, Jim Biagi, Nicole Mittmann, Jessica Arias, Scott Gavura, Kelvin Chan

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsOntario Institute for Cancer ResearchKingston Health Sciences CentrePublic Health OntarioSunnybrook HospitalCanadian Centre for Applied Research in Cancer ControlHealth Sciences CentreWomen's College HospitalCancer Care OntarioSunnybrook Health Science CentreInstitute for Clinical Evaluative SciencesUniversity of Toronto
Fundersnot available
KeywordsFOLFIRINOXMedicineGemcitabineCost effectivenessPropensity score matchingCohortPancreatic cancerRetrospective cohort studyPopulationRandomized controlled trialOncologyQuality-adjusted life yearInternal medicineCancerIrinotecanEnvironmental health

Abstract

fetched live from OpenAlex

529 Background: Currently, there are no direct randomized control trials (RCTs) comparing gemcitabine and nab-paclitaxel (Gem-Nab) and FOLFIRINOX for advanced pancreatic cancer (APC). Thus, previous model-based cost-effectiveness analyses were based on indirect comparisons of RCT data. While it is well known that RCT-based efficacy does not often translate to real-world effectiveness, there is limited literature investigating the comparative cost-effectiveness of Gem-Nab versus FOLFIRINOX for APC in the real-world. The objective of this study is to examine the real-world cost-effectiveness of Gem-Nab versus FOLFIRINOX in patients with APC in Ontario, Canada. Methods: This population-based retrospective cohort study compared all patients treated with first-line Gem-Nab or FOLFIRINOX for APC with ECOG performance status 0-1 in Ontario from April 2015 to March 2019. Patients were linked to administrative databases to identify key characteristics and costing data. Using propensity scores and a stabilizing weights method, an inverse probability of treatment weighted cohort was developed. Mean survival and total costs were calculated over a 5-year time horizon, adjusted for censoring and discounted at 1.5% (per Canadian guidelines). Incremental cost-effectiveness ratio and net monetary benefit were computed (measured in life-years and quality-adjusted life years) to estimate cost-effectiveness from the public healthcare payer’s perspective. A sensitivity analysis was conducted using the propensity score matching method. Results: 1,988 patients were identified (Gem-Nab: 928, FOLFIRINOX: 1,060). Mean survival was lower for patients in the Gem-Nab group than the FOLFIRINOX group (0.98 versus 1.26 life-years, incremental -0.28 (95% confidence interval -0.47, -0.13)). Patients in the Gem-Nab group also incurred greater mean 5-year total costs (Gem-Nab: $103,884, FOLFIRINOX: $101,518). Key cost contributors include ambulatory cancer care, acute in-patient hospitalization, and systemic therapy drug acquisition. Gem-Nab was dominated by FOLFIRINOX, as it is less effective and more costly. Results from the sensitivity analysis were similar. Conclusions: In routinely treated unselected patients, Gem-Nab is likely more costly and less effective than FOLFIRINOX and therefore, not considered cost-effective at any commonly accepted willingness-to-pay threshold.

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)0.001
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.330

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.072
GPT teacher head0.437
Teacher spread0.366 · 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".

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Citations1
Published2022
Admission routes2
Has abstractyes

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