Predicting pain reduction following laparoscopic surgery for endometriosis: a retrospective cohort study using UK national and research databases
Bibliographic record
Abstract
OBJECTIVE: To develop and validate models to predict which endometriosis patients are likely to experience pain reduction following therapeutic laparoscopy using intraoperative findings and patient characteristics. DESIGN: A retrospective secondary data analysis with patient workshops. SETTING: Analysis of a UK nationwide, specialist centre, surgical database (British Society for Gynaecological Endoscopy, BSGE) (2013-2019, N=9171) and two research databases, MEDAL (2011-2013, N=667) and LUNA (1998-2005, N=592) for exploratory analyses and external validation. PARTICIPANTS: Database patients had laparoscopically confirmed (BSGE) or clinically suspected endometriosis (MEDAL, LUNA) and ranged from 16 to 65 years. Patient workshops included UK-wide endometriosis patients from the community, secondary care doctors and endometriosis nurses. PRIMARY AND SECONDARY OUTCOME MEASURES: Following model development and internal validation, primary outcome measures included model performance statistics for discrimination (C-statistic) and calibration (calibration slope and calibration-in-the-large) for pain-improvement models for each of the five clinically meaningful pain domains. Secondary outcome measures included performance statistics for externally validated models and net benefit (using decision curve analysis). RESULTS: Following internal validation for dyspareunia (pain during sexual intercourse), non-cyclical pelvic pain (NPP), dyschezia (painful defecation) and quality of life our models showed good discrimination ability with C-statistics of 0.768, 0.750, 0.808 and 0.792, respectively. Significant increases in odds of pain relief were associated with trying to conceive for less than 18 months, any treated endometriosis of the ovary or uterosacral ligament or hysterectomy at the time of laparoscopy. For those models for which sufficient data were available to do external validation, dyspareunia and NPP showed good ability to predict pain reduction following surgery with C-statistics of 0.759 and 0.741, respectively, but after external validation only the model for dyspareunia good discriminatory ability (C-statistic=0.718). Despite this, decision curve analysis indicated some net benefit for all externally validated models. CONCLUSIONS: Clinical prediction models can help identify women who will experience pain reduction after therapeutic laparoscopy, but more work is required to externally validate the current models. Removal of ovarian and utero-sacral ligament endometriosis appears to convey pain relief after surgery, whereas removal of superficial peritoneal endometriosis does not.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".