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Comparative effectiveness and safety of the implementation of universal public funding of FOLFIRINOX (FFX) and gemcitabine (G) + nab-paclitaxel (GnP) in advanced pancreatic cancer (APC): A population-based study.

2018· article· en· W2794222772 on OpenAlexaffabout
Helen Guo, Jaclyn Beca, Ruby Redmond‐Misner, Wanrudee Isaranuwatchai, Lucy Qiao, Craig C. Earle, Scott R. Berry, James Biagi, Stephen Welch, Brandon M. Meyers, Nicole Mittmann, Natalie G. Coburn, Aliya Pardhan, Jessica Arias, Deborah E. Schwartz, Scott Gavura, Leta Forbes, Robin S. McLeod, Erin Kennedy, Kelvin Chan

Bibliographic record

VenueJournal of Clinical Oncology · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsHamilton Regional Laboratory Medicine ProgramMcMaster UniversityCancer Care OntarioQueen's UniversityMount Sinai HospitalSunnybrook HospitalHealth Sciences CentreOntario Institute for Cancer ResearchJuravinski Cancer CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineGemcitabineFOLFIRINOXPancreatic cancerCancer registryProportional hazards modelInternal medicineLogistic regressionPropensity score matchingPopulationCancerOncologyEnvironmental health

Abstract

fetched live from OpenAlex

375 Background: FFX has been universally publicly funded in Ontario, Canada, for metastatic pancreatic cancer (mPC) and unresectable locally advanced pancreatic cancer (uLAPC) since 11/2011 and 04/2015, respectively. GnP has been publicly funded for uLAPC and mPC (APC) since 04/2015. We examined the real world comparative effectiveness and safety of implementing funding of FFX and GnP for patients with APC. Methods: Patients with APC who received first-line FFX, GnP, or G from 01/2008-03/2016 were identified in CCO’s New Drug Funding Program database and divided into 3 periods: 01/2008-10/2011 (P1), 11/2011-03/2015 (P2), and 04/2015-03/2016 (P3). Data were linked with the Ontario Cancer Registry and others to ascertain demographics, comorbidities, and outcomes. Matching weights of propensity score to simultaneously compare three periods were generated using multinomial logistic regression. Crude and adjusted survival analyses were conducted to assess overall survival (OS) using Kaplan-Meier and weighted Cox regression methods.Weighted negative binomial models were used to estimate rate ratios (RR) for all-cause hospitalization (H) and ED visits. Results: We identified 3696 patients (1250 in P1, 1891 in P2, 555 in P3) (overall mean age 65, female 46%). In P2, 49% received FFX. In P3, 53% received FFX and 35% received GnP. Median OS was 5.7, 7.0, and 7.5 months for P1, P2, and P3, respectively. Median OS for FFX and GnP in mPC were 8.8 and 5.5 months, respectively. OS was improved in P2 vs. P1 (HR = 0.84, 0.78-0.90) and in P3 vs. P2 (HR = 0.82, 0.73-0.92). ED visits were similar compared P2 vs. P1 (RR=1.02, p = 0.75) and P3 vs. P2 (RR=1.04, p = 0.48), and H was reduced in P2 vs. P1 (RR = 0.86, p = 0.01), but similar in P3 vs. P2 (RR = 0.98, p = 0.78). H for febrile neutropenia (FN) was increased in P2 vs. P1 (RR = 2.18, p = 0.04) but not in P3 vs. P2 (RR = 1.32, p = 0.45). Conclusions: Implementation of universal public funding of FFX for mPC improved OS and reduced the rates H overall, but increased FN-related H. Funding of FFX for uLAPC and GnP for APC improved OS without increased in ER and H.

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)0.005
metaresearch head score (Gemma)0.018
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 score0.094
Threshold uncertainty score0.187

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.112
GPT teacher head0.433
Teacher spread0.321 · 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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Citations2
Published2018
Admission routes2
Has abstractyes

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