Hysteroscopic endometrial resection vs. hysterectomy for abnormal uterine bleeding: impact on quality of life and sexuality. Evidence from a systematic review of randomized controlled trials
Bibliographic record
Abstract
PURPOSE OF REVIEW: The aim of this systematic review is to summarize the current evidence regarding the effectiveness of hysterectomy and hysteroscopic endometrial resection in improving quality of life (QoL), sexual function and psychological wellbeing of women abnormal uterine bleeding. RECENT FINDINGS: We performed a systematic literature search in PubMed/MEDLINE and Embase for original studies written in English (registered in PROSPERO 2019 CRD42019133632), using the terms 'endometrial ablation', 'endometrial destruction', 'endometrial resection', 'hysterectomy', 'menorrhagia', 'dysfunctional uterine bleeding', 'quality of life', 'sexuality' published up to April 2019. Our literature search produced 159 records. After exclusions, nine studies were included showing the following results: both types of treatment significantly improve QoL and psychological wellbeing; hysterectomy is associated with higher rates of satisfaction; hysterectomy is not associated with a significant deterioration in sexual function. SUMMARY: Hysterectomy is currently more advantageous in terms of improving abnormal uterine bleeding and satisfaction rates than hysteroscopic endometrial destruction techniques. Furthermore, there is some evidence of a greater improvement in general health for women undergoing hysterectomy. However, high-quality prospective randomized controlled trials should be implemented to investigate the effectiveness of hysterectomy and endometrial ablation in the improvement of QoL outcomes in larger patient cohorts.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".