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Assessment of the association of the VeriStrat test with outcomes in patients (pts) with advanced pancreatic cancer (PC) treated with gemcitabine (G) with or without erlotinib (E) in the NCIC CTG PA.3 phase III trial.

2013· article· en· W2588549428 on OpenAlexaff
Daniel J. Renouf, Wendy R. Parulekar, Julia Grigorieva, Dongsheng Tu, Malcolm J. Moore

Bibliographic record

VenueJournal of Clinical Oncology · 2013
Typearticle
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health NetworkQueen's UniversityUniversity of TorontoBC Cancer Agency
Fundersnot available
KeywordsMedicineInternal medicineHazard ratioProportional hazards modelGemcitabineErlotinibPlaceboCancerOncologyGastroenterologyConfidence intervalPathologyEpidermal growth factor receptor

Abstract

fetched live from OpenAlex

4061 Background: VeriStrat is a mass spectrometry based assay performed on serum or plasma that has been shown to be prognostic in several tumor types, and may predict differential drug benefit in several settings. We investigated the association of VeriStrat with outcomes in the NCIC CTG PA.3 randomized phase III trial of G and E vs. G and placebo (P) in pts with advanced PC. Methods: Pre-treatment plasma samples were available for 499/569 (87.7%) enrolled pts. VeriStrat testing was performed in a CLIA-certified laboratory; pts were classified as either Good, Poor, or indeterminate. The relationship between VeriStrat results and overall survival (OS) and progression free survival (PFS) was assessed by Kaplan-Meier curves and log-rank test in univariate analysis and Cox model adjusting for gender, age [>60 vs. ≤60], race [Caucasian vs. other], ECOG [0-1 vs. 2], and pain intensity at baseline [≤20 vs. >20] in multivariate analysis. The predictive effect was assessed by interaction test. All statistical analyses were performed by the NCIC CTG. Results: Of the 499 samples, 11 were hemolyzed and 4 had acquisition failures. VeriStrat was performed on 484 samples, 9 failed quality control, 22 had indeterminate results. Of the remaining 452, 353 (78%) were classified as Good and 99 (22%) as Poor. In the G and P arm, median OS was 7.16 months (ms) for VeriStrat Good vs. 3.78ms for VeriStrat Poor (p<0.0001); Adjusted Hazard Ratio (AHR) 0.59 (0.43-0.82), p=0.002. In the G and E arm, median OS was 7.33ms for VeriStrat Good vs. 4.50ms for VeriStrat Poor p<0.0001; AHR 0.47 (0.32-0.70), p=0.001. A similar relationship was seen for PFS (G and P arm: median PFS 3.91 vs. 2.07ms (p=0.001); AHR 0.67 [0.49-0.92], p=0.01); G and E arm: median PFS 4.24 vs. 2.86ms (p=0.0004); AHR 0.54 [0.37-0.80], p=0.002). Tests of interaction of VeriStrat status and treatment for OS and PFS were not significant: AHR 0.78 (0.48-1.25), p=0.30 and AHR 0.80 (0.50-1.30), p=0.37 respectively. Conclusions: VeriStrat results were significantly associated with OS and PFS for both regimens in this study. VeriStrat was not predictive of benefit from the addition of E to G.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.070
GPT teacher head0.480
Teacher spread0.409 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

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