Influence of Regional Analgesia on Self-Reported Quality of Sleep After Gynecological Abdominal Surgery: A Secondary Analysis of a Randomized Trial
Bibliographic record
Abstract
OBJECTIVES: To determine whether intrathecal morphine (ITM) analgesia in abdominal surgery for presumed gynecological malignancy was associated with better self-reported sleep quality postoperatively compared with epidural analgesia (EDA), and to evaluate risk factors for bad sleep quality. METHODS: A secondary analysis of a randomized open controlled trial, comparing ITM and EDA as postoperative analgesia in 80 women undergoing laparotomy under general anaesthesia in an enhanced recovery after surgery framework. A total of 38 women allocated to ITM and 39 to EDA completed the study. The Swedish Postoperative Symptoms Questionnaire assessed symptoms and sleep quality during the first postoperative week. Multiple logistic regression models evaluated risk factors. The results are presented as adjusted odds ratios with 95% CIs. RESULTS: The sleep quality night-by-night did not differ significantly between the women who had ITM or EDA. Risk factors for bad sleep quality for night 1 were age (0.91; 0.84-0.99), operation time (1.02; 1.00-1.03), and opioid consumption (0.96; 0.91-0.99). For night 2, regular use of hypnotics preoperatively (15.81; 1.52-164.27) and opioid consumption (1.07; 1.00-1.14) were independent risk factors for bad sleep. After the second night, no risk factors were disclosed. CONCLUSIONS: ITM and EDA did not appear to affect the sleep quality postoperatively differently in women undergoing laparotomy for presumed gynecological malignancy. Risk factors for self-reported bad sleep quality varied during the first 3 days after surgery. Younger age, longer operation time, and preoperative use of hypnotics were associated with bad sleep quality, whereas the effect of opioid consumption on sleep quality varied depending on the time since surgery. These findings merit further studies.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".