Systematic review of patient-specific predictors of pain improvement to endometriosis surgery
Bibliographic record
Abstract
BACKGROUND: \nUp to 28% of endometriosis patients do not get pain relief from therapeutic laparoscopy but this subgroup is not defined. \n \nOBJECTIVES: \nTo identify any prognostic patient-specific factors (such as but not limited to patients’ type or location of endometriosis, sociodemographics and lifestyle) associated with a clinically meaningful reduction in post-surgical pain response to operative laparoscopic surgery for endometriosis. \n \n \nSEARCH STRATEGY: \nPubMed, Cochrane and Embase databases were searched from inception to 19th May 2020 without language restrictions. Backward and forward citation tracking was used. \n \n \nSELECTION CRITERIA, DATA COLLECTION AND ANALYSIS: \nCohort studies reporting prognostic factors, along with scores for domains of pain associated with endometriosis before and after surgery, were included. Studies that compared surgeries, or laboratory tests, or outcomes without stratification were excluded. Results were synthesised but variation in study designs and inconsistency of outcome reporting precluded us from doing a meta-analysis. \n \nMAIN RESULTS: \n \nFive studies were included. Quality assessment using the Newcastle Ottawa Scale graded three studies as high, one as moderate and one as having a low risk of bias. \n \nFour of five included studies separately reported that a relationship exists between more severe endometriosis and stronger pain relief from laparoscopic surgery \n \nCONCLUSION: \nCurrently there are few studies of appropriate quality to answer the research question. We recommend future studies report core outcome sets to enable meta-analysis. \n \nFUNDING: \nNIHR PB-PG-0317-20018 \n \n \nPROSPERO: \nCRD42018108604
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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 | 0.016 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".