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Record W2160834467 · doi:10.1155/2012/180574

Surgery Should Complement Endocrine Therapy for Elderly Postmenopausal Women with Hormone Receptor-Positive Early-Stage Breast Cancer

2012· article· en· W2160834467 on OpenAlexaff
Olivier Nguyen, Lucas Sidéris, Pierre Drolet, Marie‐Claude Gagnon, Guy Leblanc, Yves Leclerc, Andrew Mitchell, Pierre Dubé

Bibliographic record

VenueInternational Journal of Surgical Oncology · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEstrogen and related hormone effects
Canadian institutionsUniversité de MontréalHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsMedicineBreast cancerStage (stratigraphy)CancerInternal medicineBreast surgerySurgeryAlgorithm

Abstract

fetched live from OpenAlex

Introduction. Endocrine therapy (ET) is an integral part of breast cancer (BC) treatment with surgical resection remaining the cornerstone of curative treatment. The objective of this study is to compare the survival of elderly postmenopausal women with hormone receptor-positive early-stage BC treated with ET alone, without radiation or chemotherapy, versus ET plus surgery. Materials and Methods. This is a retrospective study based on a prospective database. The medical records of postmenopausal BC patients referred to the surgical oncology service of two hospitals during an 8-year period were reviewed. All patients were to receive ET for a minimum of four months before undergoing any surgery. Results. Fifty-one patients were included and divided in two groups, ET alone and ET plus surgery. At last follow-up in exclusive ET patients (n = 28), 39% had stable disease or complete response, 22% had progressive disease, of which 18% died of breast cancer, and 39% died of other causes. In surgical patients (n = 23), 78% were disease-free, 9% died of recurrent breast cancer, and 13% died of other causes. Conclusions. These results suggest that surgical resection is beneficial in this group and should be considered, even for patients previously deemed ineligible for surgery.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.891
Threshold uncertainty score0.480

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 head0.020
GPT teacher head0.324
Teacher spread0.305 · 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 designNot applicable
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

Citations4
Published2012
Admission routes1
Has abstractyes

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