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Record W2089464750 · doi:10.1159/000236048

Costs Associated with Capecitabine or 5-Fluorouracil Monotherapy after Surgical Resection in Patients with Colorectal Cancer

2009· article· en· W2089464750 on OpenAlexaff
Edward Chu, Nianwen Shi, Wenhui Wei, Johanna C. Bendell, Thomas H. Cartwright

Bibliographic record

VenueOncology · 2009
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Treatments and Studies
Canadian institutionsThomson Reuters (Canada)
Fundersnot available
KeywordsCapecitabineFluorouracilColorectal cancerMedicineOncologyResectionSurgical resectionInternal medicineCancerSurgery

Abstract

fetched live from OpenAlex

OBJECTIVE: To compare chemotherapy-related and total medical costs among patients with colorectal cancer (CRC) receiving capecitabine or 5-fluorouracil (5-FU) monotherapy after surgical resection. METHODS: This retrospective, claim-based study utilized the Thomson Reuters Market Scan(R) databases to identify 1,396 CRC patients who received capecitabine or 5-FU monotherapy within 90 days of surgical resection from 2003 through 2006. Propensity score matching addressed selection bias, and multivariate models estimated adjusted relative risks of treatment-related complications and medical costs of matched cohorts. RESULTS: Capecitabine users incurred USD 740 less in total direct medical costs (p = 0.003) and USD 785 less in chemotherapy-related costs (p < 0.0001) than 5-FU users. Although drug acquisition cost was higher for capecitabine than for 5-FU (USD 958 vs. USD 71, p < 0.0001), chemotherapy administration cost was lower (USD 76 vs. USD 1,062, p < 0.0001). The unadjusted (610 vs. 1,960 events per 1,000 person-months) and adjusted risks (47%) were lower for capecitabine than 5-FU for any complication, and specifically for bone marrow (67%), gastrointestinal (50%), and constitutional (41%) complications (p < 0.0001, all comparisons). CONCLUSIONS: Adjuvant capecitabine monotherapy was associated with lower total medical and chemotherapy-related costs than 5-FU. Reduced complications and costs associated with capecitabine administration offset the higher acquisition cost.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.108
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.298
Teacher spread0.287 · 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.

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

Citations14
Published2009
Admission routes1
Has abstractyes

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