Evaluate the Long-Term Efficacy and Patient Satisfaction Following Embolization of Pelvic Congestion
Bibliographic record
Abstract
Introduction: Pelvic varices are frequent, could be found in 10% of women, and 40% of them may develop pelvic congestion syndrome. The IR treatment is known to be efficient but the approaches and methods are quite different between centers and doctors, mainly to prove the effectiveness of a sequential treatment. Method(s): A retrospective study included 246 patients embolized for SCP between 2003 and 2021 combined with cross-sectional questionnaires. All adult females who had pelvic congestion syndrome embolization in our center were included, and patients lost at follow-up were excluded. Demographic data, patient's symptoms, procedural details, assessment of patient symptom evolution compared with baseline in percentage, and occurrence of complications were documented. Result(s): From 246 women (M = 42 years), 192 were included, 20% had previous treatment for leg varices. The main symptoms were pelvic pain in 79%, lower limb pain in 55%, and postcoital dyspareunia in 49%. A total of 505 procedures were performed (mean per patient = 2,09). Usually, the ovarian veins were embolized in the first. Conclusion(s): The endovascular treatment of PSC is effective and safe with 80% of long-term improvement. Best results are obtained in sequential treatment with more than one session of embolization. Publication History Article published online: 09 February 2023 © 2023. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/) Thieme Medical and Scientific Publishers Pvt. Ltd. A-12, 2nd Floor, Sector 2, Noida-201301 UP, India
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".