Association of Adipose Tissue Distribution With Type 2 Diabetes in Breast Cancer Patients
Bibliographic record
Abstract
PURPOSE: We examined the association of adipose tissue distribution with type 2 diabetes (T2D) in breast cancer patients. METHODS: Participants (N = 238) diagnosed with breast cancer at 20-75 years old who received breast cancer treatment at a major hospital from January 1, 2012, to December 31, 2017, with at least one completed and identifiable abdominal or pelvic computed tomography (CT) scan and data regarding race and ethnicity were included. Thirty-two breast cancer patients were identified as T2D patients after their breast cancer diagnoses. The adipose tissue distribution (visceral fat area [VFA], subcutaneous fat area [SFA], and the ratio of VFA to SFA [VFA/SFA]) was quantified on CT images of the third lumbar vertebra. T2D status was retrieved from patients' electronic medical records. The association of adipose tissue distribution with T2D in women with breast cancer was examined using multivariable logistic regression. RESULTS: SFA). A positive association of VFA/SFA ratio with T2D was observed (OR = 19.57, 95% CI = 3.26-117.42, per unit VFA/SFA), although the estimate was imprecise. CONCLUSIONS: The amount of subcutaneous adipose tissue was inversely associated with T2D, and the ratio of the amount of visceral adipose tissue to the amount of subcutaneous adipose tissue was positively associated with T2D in breast cancer patients.
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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) | 0.002 | 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".