Adhesion prevention in gynaecological surgery
Bibliographic record
Abstract
OBJECTIVES: To review the etiology and incidence of and associative factors in the formation of adhesions following gynaecological surgery. To review evidence for the use of available means of adhesion prevention following gynaecological surgery. OPTIONS: Women undergoing pelvic surgery are at risk of developing abdominal and/or pelvic adhesive disease postoperatively. Surgical technique and commercial adhesion prevention systems may decrease the risk of postoperative adhesion formation. OUTCOMES: The outcomes measured are the incidence of postoperative adhesions, complications related to the formation of adhesions, and further intervention relative to adhesive disease. EVIDENCE: Medline, EMBASE, and The Cochrane Library were searched for articles published in English from 1990 to March 2009, using appropriate controlled vocabulary and key words. Results were restricted to systematic reviews, randomized control trials/controlled clinical trials, cohort studies, and meta-analyses specifically addressing postoperative adhesions, adhesion prevention, and adhesive barriers. Searches were updated on a regular basis and incorporated in the guideline to March 2009. Grey (unpublished) literature was identified through searching the websites of health technology assessment and health technology assessment-related agencies, clinical practice guideline collections, clinical trial registries, and national and international medical specialty societies. VALUES: The quality of evidence was rated using the criteria described in the Report of the Canadian Task Force on Preventive Health Care.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".