Prediction of the progression of endometrial hyperplasia in women of premenopausal and menopausal age based on an analysis of clinical and anamnestic indicators using multiparametric neural network clustering
Bibliographic record
Abstract
AMA Selskyy P, Sverstiuk A, Slyva A, Selskyi B. Prediction of the progression of endometrial hyperplasia in women of premenopausal and menopausal age based on an analysis of clinical and anamnestic indicators using multiparametric neural network clustering. Family Medicine & Primary Care Review. 2023;25(2):184-189. doi:10.5114/fmpcr.2023.127679. APA Selskyy, P., Sverstiuk, A., Slyva, A., & Selskyi, B. (2023). Prediction of the progression of endometrial hyperplasia in women of premenopausal and menopausal age based on an analysis of clinical and anamnestic indicators using multiparametric neural network clustering. Family Medicine & Primary Care Review, 25(2), 184-189. https://doi.org/10.5114/fmpcr.2023.127679 Chicago Selskyy, Petro, Andrii Sverstiuk, Andrii Slyva, and Boryslav Selskyi. 2023. "Prediction of the progression of endometrial hyperplasia in women of premenopausal and menopausal age based on an analysis of clinical and anamnestic indicators using multiparametric neural network clustering". Family Medicine & Primary Care Review 25 (2): 184-189. doi:10.5114/fmpcr.2023.127679. Harvard Selskyy, P., Sverstiuk, A., Slyva, A., and Selskyi, B. (2023). Prediction of the progression of endometrial hyperplasia in women of premenopausal and menopausal age based on an analysis of clinical and anamnestic indicators using multiparametric neural network clustering. Family Medicine & Primary Care Review, 25(2), pp.184-189. https://doi.org/10.5114/fmpcr.2023.127679 MLA Selskyy, Petro et al. "Prediction of the progression of endometrial hyperplasia in women of premenopausal and menopausal age based on an analysis of clinical and anamnestic indicators using multiparametric neural network clustering." Family Medicine & Primary Care Review, vol. 25, no. 2, 2023, pp. 184-189. doi:10.5114/fmpcr.2023.127679. Vancouver Selskyy P, Sverstiuk A, Slyva A, Selskyi B. Prediction of the progression of endometrial hyperplasia in women of premenopausal and menopausal age based on an analysis of clinical and anamnestic indicators using multiparametric neural network clustering. Family Medicine & Primary Care Review. 2023;25(2):184-189. doi:10.5114/fmpcr.2023.127679.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".