Prediction of ischemic pain following uterine artery embolization for fibroids
Bibliographic record
Abstract
Objective. To identify predictive factors for ischemic pain following uterine artery embolization (UAE) for treatment of uterine fibroids. Study design. A retrospective analysis of patients undergoing UAE in a prospective single arm trial, the Ontario Uterine Fibroid Embolization Study. Patients were classified into low and high pain groups. Multivariate logistic regression (LR), using a forward stepwise approach, was used to identify predictors of high pain (HP) after UAE. Results. The variables found to be related to HP in univarite LR were age <40 years, patients without children, currently employed, uterine size ≥1001 cm3 and dominant fibroid size ≥500 cm3. The final model predicts that patients <40 years (OR = 1.95, p < 0.01), with a dominant fibroid size ≥500 cm3 (OR = 1.63, p = 0.06), and/or who currently are employed (OR = 1.79, p = 0.04) have higher odds of experiencing HP after UAE. Conclusion. Patients who are younger than 40 years, have fibroids size larger than 500 cm3, and/or are currently employed have a higher likelihood of experiencing HP after the UAE procedure. The model needs to be validated in future studies.
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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 | 0.001 | 0.005 |
| 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) | 0.001 | 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".