Spinal Anesthesia Prior to Laparoscopic Hysterectomy Resulted in Decreased Postoperative Pain and Opioid Use
Bibliographic record
Abstract
Study Objective: To determine if a pre-operative morphine/bupivacaine spinal injection prior to laparoscopic hysterectomy reduced postoperative pain and resulted in less opioid consumption during the hospital stay.Methods: A retrospective cohort study (Canadian Task Force Classification II-2) was conducted at a single institution regional referral center (community hospital) in North Carolina.Three hundred nineteen patients met criteria for inclusion: 192 received spinal anesthesia and 127 did not.Baseline demographics were similar between the two groups.Median pain scores were significantly lower in the treatment than the control group on day of surgery (DOS) (2 vs. 6; P < 0.001) and postoperative day 1 (POD1) (2 vs. 4; P < 0.001).Results: Primary outcomes were pain scores on DOS and POD1 and inpatient opioid use.Pain scores were obtained using the 0 to 10 Numerical Rating Scale.Opioids were converted to oral morphine milliequivalents (OME).Median opioid use was also significantly lower in the treatment than the control group on DOS (0 vs. 15.00OME; P < 0.001) and POD1 (0 vs. 7.5 OME; P < 0.001).Median length of stay between the groups was not significantly different.Conclusion: Pre-operative morphine spinal injection for laparoscopic hysterectomy led to significantly lower pain scores and inpatient opioid consumption.Pre-operative spinal anesthesia for benign laparoscopic hysterectomy appears helpful for enhancing the postoperative experience.
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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".