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Relationship between body mass index (BMI) at diagnosis of ER+ node negative breast cancer (BC) and Oncotype DX recurrence score.

2012· article· en· W2598794769 on OpenAlexaff
Caroline Lohrisch, Ashley Davidson, Stephen Chia, Karen A. Gelmon, Tamara N. Shenkier, Susan Ellard, Ryan Woods, Linda Wong, Janice Pope

Bibliographic record

VenueJournal of Clinical Oncology · 2012
Typearticle
Languageen
FieldMedicine
TopicCancer Risks and Factors
Canadian institutionsInterior HealthBC Cancer Agency
Fundersnot available
KeywordsMedicineOverweightBody mass indexBreast cancerInternal medicineCancerObesityOncologyStage (stratigraphy)Gynecology

Abstract

fetched live from OpenAlex

582 Background: Inferior stage-adjusted survival for BC among obese (O) women has been reported. This may reflect differences in treatment planned for O women, in treatment received (due to toxicity), or in tumor biology arising in different cellular environments, such as high serum insulin levels, which are associated with a higher risk of recurrence. Methods: We examined whether overweight (BMI 25-30) or obese (BMI >30) women had a higher Oncotype Dx Recurrence Score (RS) after ER+, node negative (NN) or N0i+ BC than normal weight women. Included were all participants at the BC Cancer Agency in a RS clinical utility study in consecutive ER+ NN, N0i+ BC (n=156) at the BC Cancer Agency and in the TAILORx trial (n=56). Sixteen were excluded (n=5 withdrew consent; 1 triple negative; 3 test failed; 2 her2+; 1 neoadjuvant treatment, n=4 height and weight missing). Results: A similar proportion of O patients had low, intermediate, and high RS tumors. Cancers with a high RS were more likely to be grade 3 (55% of grade 3 tumours had high RS, 33% intermediate RS, 12% low RS) and fewer were strongly ER positive (69% of high RS versus 97% of intermediate and low RS). Conclusions: While this data does not support differences in tumor risk arising in O versus non obese environments in ER+, NN BC, examination of a larger data set may be more informative. [Table: see text]

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)0.002
metaresearch head score (Gemma)0.003
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 score0.026
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
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 integrity0.0000.001
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 head0.215
GPT teacher head0.491
Teacher spread0.276 · 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

Citations4
Published2012
Admission routes1
Has abstractyes

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