Long-Lasting Analgesic Effects of Intraoperative Thoracic Epidural With Bupivacaine for Liver Resection
Bibliographic record
Abstract
OBJECTIVES: Continuous epidural analgesia may be considered in liver resection but is often avoided because of possible coagulopathies and the risk of epidural hematoma in the postoperative period. On the other hand, there is no coagulation defect during the surgery. Effective prevention of postoperative pain may require continuous sensory ablation throughout the surgery event. METHODS: A prospective, randomized, double-blind study was conducted to evaluate the efficacy of intraoperative epidural anesthesia on postoperative morphine consumption via patient-controlled analgesia after liver surgery in 2 groups of patients. One group (epidural) received, intraoperatively, thoracic epidural bupivacaine perfusion (0.5% at 3 mL/hr) added to preoperative intrathecal morphine (0.5 mg) and fentanyl (15 microg). The other group (placebo) was administered the same intrathecal narcotics but with a sham epidural. Forty-four patients scheduled for major liver resection (> or =2 segments) were recruited. Patient-controlled analgesia morphine consumption, pain at rest and with movement, sedation, nausea, pruritus, and respiratory frequency were evaluated at 6, 9, 12, 18, 24, 36, and 48 hrs after intrathecal morphine injection. RESULTS: Patients in the placebo group consumed twice as much morphine during each time interval than patients in the epidural group (at 48 hrs: 123 [SD, 46] vs 59 [SD, 25] mg; P < 0.0001). Pain evaluation on visual analog scale at rest and on movement was lower in the epidural group (P = 0.017 and P = 0.037). CONCLUSION: Intraoperative thoracic epidural infusion of bupivacaine, added to intrathecal morphine, decreased postoperative morphine consumption with better pain relief compared with the placebo.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".