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Record W4391917303 · doi:10.14701/ahbps.23-107

Survival benefit of neoadjuvant FOLFIRINOX for patients with borderline resectable pancreatic cancer

2024· article· en· W4391917303 on OpenAlexaff
Evelyn Waugh, Juan Glinka, Daniel Breadner, Rachel Q. Liu, Ephraim Tang, Laura Allen, Stephen Welch, Ken Leslie, Anton Skaro

Bibliographic record

VenueAnnals of Hepato-Biliary-Pancreatic Surgery · 2024
Typearticle
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsWestern University
Fundersnot available
KeywordsFOLFIRINOXMedicineInterquartile rangeHazard ratioPancreatic cancerConfidence intervalNeoadjuvant therapyPerioperativeInternal medicineProportional hazards modelOncologySurgeryCancerOxaliplatinColorectal cancer

Abstract

fetched live from OpenAlex

Backgrounds/Aims: While patients with borderline resectable pancreatic cancer (BRPC) are a target population for neoadjuvant chemotherapy (NAC), formal guidelines for neoadjuvant therapy are lacking. We assessed the perioperative and oncological outcomes in patients with BRPC undergoing NAC with FOLFIRINOX for patients undergoing upfront surgery (US). Methods: The AHPBA criteria for borderline resectability and/or a CA19-9 level > 100 μ/mL defined borderline resectable tumors retrieved from a prospectively populated institutional registry from 2007 to 2020. The primary outcome was overall survival (OS) at 1 and 3 years. A Cox Proportional Hazard model based on intention to treat was used. A receiver-operator characteristics (ROC) curve was constructed to assess the discriminatory capability of the use of CA19-9 > 100 μ/mL to predict resectability and mortality. Results: = 0.001). CA19-9 > 100 μ/mL showed poor discrimination in predicting mortality, but was a moderate predictor of resectability. Conclusions: We found a survival benefit of NAC with FOLFIRINOX for BRPC. Greater pre-treatment of CA19-9 and multivessel involvement on initial imaging were associated with progression of the disease following NAC.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.067
GPT teacher head0.363
Teacher spread0.296 · 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 teacher head, not a consensus.

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

Citations6
Published2024
Admission routes1
Has abstractyes

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