The association between body mass index and molecular subtypes in endometrial carcinoma
Bibliographic record
Abstract
Objective: This study aims to investigate the relationship between body mass index (BMI) and molecular subtypes of endometrial carcinoma using an immunohistochemistry (IHC)-based classification approach. Methods: We analyzed a consecutive series of endometrial cancer cases undergoing surgical staging in southern Alberta (2019-2021). Molecular classification was determined through IHC-based molecular typing, incorporating p53 and mismatch repair (MMR), and further characterized with the addition of ER and PR. BMI associations with molecular classification were assessed using t-tests. Hormone receptor status was further examined in a separate cohort of MMRd endometrial cancer patients undergoing surgical staging at Foothills Medical Centre (Alberta, Canada). Results: . While there were no significant BMI differences between FIGO grade 1 and grade 2/3 tumours in the pNSMP or MMRd, a trend toward higher BMI in grade 1 tumours versus grade 2/3 tumours in the MMRd was observed (p = 0.13). A separate cohort of 53 MMRd endometrial carcinomas revealed that FIGO grade 1 tumours were associated with higher BMI (p < 0.05) and more frequent ER/PR expression compared to grade 2/3 tumours (p < 0.05). Conclusions: This study suggests an association between obesity and NSMP endometrial carcinoma. The relationship between BMI and low-grade MMRd endometrial carcinomas with increased ER/PR expression warrants further exploration.
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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".