Multi-cohort ensemble learning framework for vaginal microbiome-based endometrial cancer detection
Bibliographic record
Abstract
Introduction: Endometrial cancer is the most common gynecological malignancy in high-income countries and lacks an established strategy for early detection. Prior studies suggest that the vaginal microbiome may hold diagnostic potential, but inconsistent findings have limited clinical translation. Methods: We conducted a systematic review to collect and analyze vaginal 16S rRNA sequencing data from five independent cohorts (n = 265). These studies included women with histologically confirmed endometrial cancer and controls with benign gynecologic conditions. We used these datasets to identify microbial signatures associated with endometrial cancer and to develop a predictive machine learning model. Results: was reproducibly enriched in cancer samples across cohorts. An ensemble classifier accurately identified endometrial cancer in a held-out test set, achieving an area under the receiver operating characteristic curve of 0.93 (95% CI: 0.71-0.93), sensitivity of 1.0 (95% CI: 0.74-1.0), and a negative predictive value of 1.0 (95% CI: 0.59-1.0). Discussion: These findings support the potential of vaginal microbiome profiling as a minimally invasive approach for early detection of endometrial 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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".