Complications associated with the use of mesh to treat female urinary incontinence and pelvic organ prolapse
Bibliographic record
Abstract
Complications can result from the use of mesh to treat female stress urinary incontinence or pelvic organ prolapse. While some of these complications are also common to non-mesh-based procedures, the risk of vaginal exposure of mesh, or mesh extrusion into other pelvic organs are unique complications that must be considered when using mesh. Chronic pain after mesh-based procedures can be a difficult complication to manage and may occur. Patients with stress incontinence or prolapse that are considering surgical treatment should be counseled on the potential complications and the likelihood of them occurring. Although there has been focus on the development, introduction and regulation of mesh, the diagnostic clinical decision-making process has not been put under scrutiny, and likely significantly impacts patient outcomes. There is a need to develop patient reported outcome measures for mesh procedures, and further work is needed to create a standardized way to measure and communicate different types of mesh-related complications. Future research to focus on the diagnostic clinical decision making process is recommended, including education on the ‘whole journey’ of the patient during the perioperative pathway.
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".