Prognostic significance of body mass index in breast cancer patients with hormone receptor-positive tumours after curative surgery
Bibliographic record
Abstract
PURPOSE: Obesity has been recognized as a significant risk factor for postmenopausal breast cancer. The aim of this study is to investigate the prognostic significance of body mass index (BMI) in hormone receptor-positive, operable breast cancer. METHODS: In this retrospective cohort study, 1,192 consecutive patients with curative resection of primary breast cancer were enrolled. Patients were assigned to two groups according to BMI: normal or underweight (BMI < 23.0 kg/m²) and overweight or obese (BMI ≥ 23.0 kg/m²). Associations among BMI and clinicopathological characteristics and prognosis of patients were assessed. RESULTS: A high BMI was significantly (P < 0.01) correlated with age, nodal stage, ALNR, ER positivity, PR positivity and menopausal status at diagnosis. Univariate analysis revealed that BMI, pathologic T stage, nodal stage, axillary lymph node ratio (ALNR) and adjuvant radiotherapy history were significantly (P < 0.05) associated with disease-free survival and overall survival, irrespective of tumour hormone receptor status. Multivariate analysis revealed BMI as an independent prognostic factor in all cases and in hormone receptor-positive cases. CONCLUSION: A high BMI (≥ 23.0 kg/m²) is independently associated with poor prognosis in hormone receptor-positive breast cancer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".