Factors Affecting Early Surgical Intervention After Uterine Artery Embolization
Bibliographic record
Abstract
UNLABELLED: Uterine artery embolization (UAE) is an effective technique for the management of uterine myoma. However, complications of this procedure can be serious, including uterine infection and bowel necrosis in conjunction with necrosis of subserous or pedunculated myomas. Treatment failure is more likely to occur in the presence of submucosal myoma associated with a uterine infection or a large myoma of more than 8 cm. Accordingly, patients whose primary symptoms include submucosal myoma and menorrhagia are best treated with a hysteroscopic myomectomy or hysterectomy. The role of the gynecologist is crucial for most effective management and safe use of uterine artery embolization. TARGET AUDIENCE: Obstetricians & Gynecologists, Family Physicians LEARNING OBJECTIVES: After completion of this article, the reader will be able to list the complications of uterine artery embolization for fibroids, to describe postembolization syndrome, and identify the myomas that are more likely to fail uterine artery embolization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 0.001 |
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".