Association Between Obesity and Clinicopathological Profile of Patients with Newly Diagnosed Non-Metastatic Breast Cancer in Saudi Arabia
Bibliographic record
Abstract
Purpose: Obesity is prevalent in Saudi Arabia and is associated with adverse clinical features and poor breast cancer (BC) outcomes. We determined the distribution of body mass index (BMI) and evaluated its association with disease characteristics and outcomes in women with non-metastatic BC. Patients and Methods: ). Regression analysis was used to evaluate clinicopathological factors associated with BMI and clinical stage. Results: ). Most patients were premenopausal (63.6%), nearly half of the patients had stage III disease, and 11.2% were screen-detected. The prevalence of obesity was 53.4%, with a significant difference between the peri/premenopausal (49.4%) and postmenopausal (61.7%) groups (p < 0.001). Obese patients were more likely to be aged >40 years, be postmenopausal, have a history of oral contraceptive pills, have advanced-stage disease, and have undergone radiation therapy, and were less likely to have human epithelial growth factor 2 (HER2)+ disease than non-obese patients. Premenopausal obese women had fewer hormone receptor-positive and more triple-negative cancers than postmenopausal obese women did. Obesity, non-screening-detected BC, and HER+ status were independent prognostic factors for advanced-stage presentation. Conclusion: The prevalence of obesity and its significant association with advanced BC justify the upscaling of screening services and instituting weight-reduction strategies.
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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 | 0.000 | 0.000 |
| 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".