Assessment of Port-Specific Pain After Gynecological Laparoscopy: A Prospective Cohort Clinical Trial
Bibliographic record
Abstract
OBJECTIVE: To determine the association between pain related to laparoscopic port sites and different incision sizes after gynecological laparoscopy. DESIGN: Prospective, cohort trial Canadian Task Force classification 2-II. SETTING: Zhejiang Provincial People's Hospital, China. PATIENTS: Two hundred patients who underwent three-port laparoscopic gynecological procedures for benign indications. INTERVENTIONS: In total, 200 patients underwent laparoscopic gynecological procedures. Each patient had three incisions, one in the left lower abdomen, measuring 5, 10, or 15 mm based on the type of surgery, another measuring 10 mm in the umbilical port, and the third one measuring 5 mm, in the right lower abdomen. Port-related pain was registered and measured by visual analogue score (VAS). MEASUREMENTS AND MAIN RESULTS: The VAS score showed statistically significant differences between 5-, 10-, and 15-mm port sites at each time point (24 and 72 hours) (P < .05); the score elevated as the size of the incision increased. Pain was significantly lower at the umbilical port sites at 24 hours than in the left lower abdominal port sites with incisions of the same (10 mm) size (P = .013) and also significantly lower in the right lower abdominal port sites than in the left lower abdominal port sites with incisions of the same (5 mm) size (P = .041). Specimen extraction port significantly affected the 24-hour pain intensity, while specimen extraction port, surgical time, and previous abdominal surgery affected the 72-hour pain intensity. CONCLUSIONS: The size of port sites is the most important factor related to port-specific pain.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".