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Record W2069834221 · doi:10.3747/co.v16i3.377

Optimal Use of Taxanes in Metastatic Breast Cancer

2009· article· en· W2069834221 on OpenAlexaffvenueabout
Karen King, Sasha Lupichuk, Lubna Baig, Molly Webster, S. Basi, David Whyte, S. Rix

Bibliographic record

VenueCurrent Oncology · 2009
Typearticle
Languageen
FieldMedicine
TopicCancer Treatment and Pharmacology
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineTaxaneMetastatic breast cancerBreast cancerOncologyGuidelineInternal medicineAnthracyclineClinical trialDocetaxelCancerRandomized controlled trialIntensive care medicinePathology

Abstract

fetched live from OpenAlex

The role of taxanes in the treatment of breast cancer is becoming increasingly important. In clinical practice, the taxanes are now standard therapy in both early-stage and metastatic breast cancer. Since the 1990s, multiple randomized clinical trials have been evaluating the efficacy of taxanes in the treatment of metastatic breast cancer. These trials have included treatment with taxanes alone or in combination with other chemotherapeutic agents. Pre-existing published guidelines for the use of taxanes in the management of metastatic breast cancer are available. The mandate of the Alberta Cancer Board Provincial Breast Tumour Group Guideline Panel was to consider and adapt the recommendations of the existing guidelines and to develop de novo guidelines to account for current evidence. For this task, the panel used the ADAPTE process, which is a systematic process of guideline adaptation developed by the ADAPTE Collaboration.The recommendations formulated by the panel included the identification of taxane regimens that could be offered in anthracycline-naïve patients, anthracycline-pretreated or -resistant patients, and patients overexpressing the human epidermal growth factor receptor 2. Potential toxicities and benefits in terms of time to progression, progression-free survival, overall survival, and quality of life were also considered.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.628
Threshold uncertainty score0.838

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)0.0010.000

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 head0.212
GPT teacher head0.492
Teacher spread0.280 · 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

Citations35
Published2009
Admission routes3
Has abstractyes

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