The role of peritoneal lavage in benign gynecologic laparoscopic surgery.
Bibliographic record
Abstract
OBJECTIVE: Laparoscopic surgery offers many advantages compared to invasive surgery but one of the main problems is postoperative pain, partially resulting from the peritoneal inflammatory process mediated by inflammatory cytokines. The rationale of this study is that intraperitoneal washing could remove inflammatory mediators that are the cause of postoperative pain and could help in the removal of CO2 from the abdominal cavity. This article aims to analyze the effects of peritoneal lavage in the reduction of postoperative shoulder pain. PATIENTS AND METHODS: 277 patients enrolled to undergo laparoscopic gynecologic surgery were included in the study. Women are randomized into two groups, according to the use or non-use of peritoneal lavage with saline solution at the end of laparoscopic gynecological major procedures. RESULTS: Data show that the peritoneal lavage can significantly reduce postoperative pain in the first 36 hours after surgery, as well as patients' requests for analgesics: during the first 3 postoperative days, requests for paracetamol were lower in the YW (Yes Washing) group than the NW (No Washing) group (77 vs. 101; p<0.05); similar results are obtained considering ketorolac administration (62 vs. 71; p<0.05). CONCLUSIONS: Peritoneal lavage after gynecological laparoscopic procedures may be effective in the reduction of postoperative pain and use of analgesics.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".