Uterus preservation is superior to hysterectomy when performing laparoscopic lateral suspension with mesh
Bibliographic record
Abstract
INTRODUCTION AND HYPOTHESIS: We aimed to compare differences between laparoscopic lateral suspension with mesh (LLS) performed with supracervical hysterectomy (LLSHE) and without hysterectomy (LLSUP). METHODS: We retrospectively collected data from women operated by a single surgeon between 2003 and 2011. From a total of 339 women with symptomatic anterior and/or apical pelvic organ prolapse (POP) and an intact uterus, 224 had LLSUP (70.4%) and 94 had LLSHE (29.6%). Three hundred and sixteen patients were examined at 1 year. Primary outcomes were objective and subjective success at 1 year during clinical evaluation. Secondary outcomes were complications (Clavien-Dindo scale) and mesh exposure. Patient satisfaction was evaluated by telephone interview using a 10-point scale and the Patient Global Impression of Improvement Scale (PGI-I). RESULTS: LLSUP and LLSHE did not differ for age (mean 57 and 55 years, respectively), preoperative status, complications, and participation at the interview (52 vs 53%). LLSHE is associated with higher mesh exposure (6.5 vs 1.3%, p = 0.014) and more frequent use of Mersilene. Titanium-coated and noncoated polypropylene was more frequently used in LLSUP. At 1 year, both anatomic success rate for the anterior compartment (98.7 vs 94.6%, p = 0.021) and subjective success rate (83.5 vs 72.8%, p = 0.035) were higher for LLSUP. Without hysterectomy, patients more often improved (90.5 vs 76.5%, p = 0.013) and would more frequently recommend the procedure (94.5 vs 80.4%, p = 0.004). CONCLUSIONS: LLS with or without hysterectomy is a safe technique with high patient satisfaction. The uterus-preserving approach appears to result in better anatomic outcome for the anterior compartment, better subjective outcome, and higher patient satisfaction.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".