Use of Paracervical Block Before Laparoscopic Supracervical Hysterectomy
Bibliographic record
Abstract
Background and Objective: Some patients who undergo laparoscopic hysterectomy request overnight admission for pain management, thus increasing costs for a surgery that is safe for same-day discharge.We wanted to evaluate whether a paracervical block of bupivacaine with epinephrine before laparoscopic supracervical hysterectomy would decrease overnight admission rates, postoperative pain, and pain medication requirement Methods: This was a randomized, double-blind, placebocontrolled, parallel-group trial (Canadian Task Force classification I) at an academic medical center.Patients undergoing laparoscopic supracervical hysterectomy were randomized to a 20-mL paracervical injection of either 0.25% bupivacaine with epinephrine or 20 mL normal saline before skin incision.All providers, except the circulating nurse, were blinded.The primary outcome was overnight hospital admission rate.Secondary outcomes included postoperative pain medication use and pain scores.Analysis included t test, 2 , Wilcoxon, and ANOVA.Results: One hundred thirty-two patients were enrolled-68 in the treatment group and 64 in the placebo group.Demographics were similar between groups.The unplanned overnight admission rate was 34% for the treatment group and 27% for the placebo group (P ϭ .25).After discharge, the treatment group used on average 8.5 tablets of narcotics, whereas the placebo group used 11.7 tablets (P ϭ .07).The treatment group took 13.1 tablets of nonnarcotic analgesics compared to 11.2 in the placebo group (P ϭ .57).Both groups reported similar pain scores.Conclusion: Paracervical block with bupivacaine and epinephrine before laparoscopic supracervical hysterectomy did not decrease overnight admission rate or affect postoperative pain.Postoperative opiate use was minimally decreased.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".