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The Role of Capecitabine in First-Line Treatment for Patients with Metastatic Breast Cancer

2006· review· en· W2130207808 on OpenAlexaff
Karen A. Gelmon, Arlene Chan, Nadia Harbeck

Bibliographic record

VenueThe Oncologist · 2006
Typereview
Languageen
FieldMedicine
TopicCancer Treatment and Pharmacology
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsMedicineCapecitabineMetastatic breast cancerOncologyInternal medicineBreast cancerCancerColorectal cancer

Abstract

fetched live from OpenAlex

Capecitabine is an important drug in the therapeutic armamentarium for metastatic breast cancer. A comprehensive worldwide clinical trial program involving >10,000 patients with locally advanced and metastatic breast cancer has provided evidence for the current treatment strategies. On the basis of data demonstrating consistent activity across several trials in patients with heavily pretreated breast cancer, capecitabine was approved in the U.S. in 1998 for the treatment of patients with metastatic disease resistant to paclitaxel and anthracycline-containing therapy, with later European Union approval for single-agent capecitabine in the metastatic setting. Capecitabine plus docetaxel (XT) was approved by the U.S. Food and Drug Administration for the treatment of metastatic breast cancer in 2001 on the basis of the large phase III trial comparing XT with docetaxel alone, which showed a survival advantage for combination therapy compared with single-agent therapy. This was shortly followed by European approval for the combination in metastatic breast cancer. The clinical utility of capecitabine in the management of breast cancer is supported by its convenient oral dosing schedule and favorable safety profile, as well as its excellent clinical activity in primary and metastatic breast cancer. Recently, clinical trials have studied single-agent capecitabine as first-line treatment and evaluated other capecitabine-containing combinations with cytotoxic and novel targeted agents.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score0.703

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.050
GPT teacher head0.394
Teacher spread0.344 · 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 designOther design
Domainnot available
GenreReview

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

Citations41
Published2006
Admission routes1
Has abstractyes

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