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CAPOX vs. FOLFOX for Colorectal Cancer - Real World Outcomes in Ontario, Canada

2025· preprint· en· W4411855763 on OpenAlexaboutno aff
Deepro Chowdhury, Gregory R. Pond, John R. Goffin

Bibliographic record

VenuePreprints.org · 2025
Typepreprint
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsnot available
Fundersnot available
KeywordsFOLFOXColorectal cancerInternal medicineMedicineOxaliplatinOncologyCancerGeneral surgery

Abstract

fetched live from OpenAlex

CAPOX and FOLFOX are widely-used chemotherapy regimens for colorectal cancer (CRC). The superiority of one regimen over the other in a real-world setting (RWE) could have significant clinical implications given their common use, but such RWE is limited. This study analyzed provincial database records of 13,461 Canadian patients treated from 2005-2017. The primary outcomes were rates of Emergency Department visits and/or hospitalizations (ED/H) and overall survival (OS). CAPOX was used less frequently (8.4%) than FOLFOX (91.6%), often in older patients (p< 0.003 for Stage I-III; p< 0.001 for Stage IV). CAPOX recipients had shorter treatment durations (median 15 vs. 20 weeks, p=0.002) and higher unadjusted ED/H rates (60.8% vs. 50.9%, p< 0.001), though this difference was nonsignificant on multivariate analysis (MVA) (HR 1.05 (0.92, 1.20), p = 0.466). Patients receiving CAPOX had worse OS than those on FOLFOX, (5-year OS 70.1% vs. 77.2% (p< 0.001) non-metastatic; 16.6% vs. 33.2% (p< 0.001) metastatic). MVA confirmed inferior OS with CAPOX (HR 1.42, p< 0.001). Other predictors of shorter OS included older age, male sex, comorbidities, rural residence, and lower income. This administrative data is at risk of bias but highlights the need for careful patient selection and informed treatment decision-making.

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)0.004
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 score0.096
Threshold uncertainty score0.694

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.004
Science and technology studies0.0030.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.083
GPT teacher head0.355
Teacher spread0.271 · 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
Published2025
Admission routes1
Has abstractyes

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