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Record W2001633715 · doi:10.1002/cncr.24114

Adjuvant targeted therapy in early breast cancer

2009· review· en· W2001633715 on OpenAlexaff
John R. Mackey, Deanna McLeod, Joseph Ragaz, Karen A. Gelmon, Sunil Verma, Kathleen I. Pritchard, Kara Laing, Louise Provencher, Lauren F. Charbonneau

Bibliographic record

VenueCancer · 2009
Typereview
Languageen
FieldMedicine
TopicHER2/EGFR in Cancer Research
Canadian institutionsSunnybrook Health Science CentreDr. H. Bliss Murphy Cancer CentreBC Cancer AgencyMcGill UniversityAlberta Cancer Foundation
Fundersnot available
KeywordsTrastuzumabMedicineCardiotoxicityOncologyBreast cancerInternal medicineAdjuvantCancerAdjuvant therapyClinical trialChemotherapy

Abstract

fetched live from OpenAlex

For this review, the authors appraised the evidence for adjuvant trastuzumab therapy in early breast cancer. There was level 1 evidence to support the routine use of 1 year of adjuvant trastuzumab in conjunction with chemotherapy for women with human epidermal growth factor receptor 2 (HER-2)-positive early breast cancer. The relative benefits of concurrent versus sequential administration remained unclear; however concurrent administration permitted the earliest possible intervention with trastuzumab with possible superiority. There was evidence to support the use of trastuzumab in both lymph node-positive and high-risk lymph node-negative patients, and preliminary data suggested that all patient subgroups that were eligible for the trials benefit equally from trastuzumab. Adjuvant trastuzumab was associated with a risk of cardiotoxicity, the long-term impact of which remains largely unknown. Routine cardiac risk assessment considering left ventricular ejection fraction, age, and prior history of cardiac events is recommended along with the selection of trastuzumab-based regimens that minimize cardiotoxicity. Trastuzumab acquisition costs for 1 year of therapy were the largest component of treatment costs.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.125
GPT teacher head0.473
Teacher spread0.348 · 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.

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

Citations36
Published2009
Admission routes1
Has abstractyes

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