Development of a Model for the Prediction of Treatment Response of Uterine Leiomyomas after Uterine Artery Embolization
Bibliographic record
Abstract
Background: Uterine artery embolization (UAE) is one of the minimally-invasive alternatives to hysterectomy for treatment of uterine leiomyomas. There are various factors affecting the outcomes of UAE, but these have only been sporadically studied. Study Objective: To identify factors associated with the efficacy of UAE for the treatment of uterine leiomyoma, and to develop a model for the prediction of treatment response of uterine leiomyomas to UAE. Study design: A retrospective cohort study (Canadian Task Force Classification II-2) Patients: One hundred ninety-eight patients with symptomatic uterine leiomyomas. Intervention: UAE Measurements and Main Results: Among 198 leiomyoma patients who were treated with UAE, 104 who underwent pelvic magnetic resonance imaging (MRI) with diffusion-weighted imaging were selected for developing prediction model. Variables that were statistically significant from the univariate analysis were: location of leiomyoma, total number of lesions, sum of leiomyomas diameters, T2 signal intensity of largest leiomyoma, and T2 leiomyoma:muscle ratio. After a logistic regression analysis, leiomyoma location and T2 signal intensity of the largest leiomyoma were found to be statistically significant variables. Using intramural myomas defined as controls, submucosal leiomyomas showed a greater response to UAE with an odds ratio of 7.6904. The odds ratio of T2 signal intensity with an increase in signal intensity of 10 was 1.093. Using these two variables, we developed a prediction model. The AUC in the prediction model was 0.833, and the AUC in the validation set was 0.791.
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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 | 0.001 | 0.000 |
| 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) | 0.000 | 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".