Effect of morphine added to multimodal cocktail on infiltration analgesia in total knee arthroplasty
Bibliographic record
Abstract
BACKGROUND: The local injection of multimodal cocktail is currently commonly used in the treatment of postoperative pain after total knee arthroplasty (TKA). It is still inconclusive whether the morphine added to the intraoperative injection mixture could make some difference. This meta-analysis aimed to evaluate the efficacy and safety of additional morphine injection on postoperative analgesia in TKA, and provide some useful information on morphine usage in clinical practice. METHODS: The randomized controlled trials (RCTs) in databases including PubMed, Web of Science, Embase, Cochrane Library, Chinese biomedical literature database (CBM), and Chinese National Knowledge Infrastructure (CNKI) databases were systematically searched. Of 623 records identified, 8 RCTs involving 1093 knees were eligible for data extraction and meta-analysis according to criteria included. RESULTS: Meta-analysis showed that the use of local morphine injection was not associated with significant pain relief within 48 hours postoperatively at rest and on motion (P > .05, all). The use of morphine reduced postoperative total systemic opioids consumption (P < .05). This study found no significant differences in other outcomes including knee flexion range of motion (ROM) (P > .05), extension ROM (P > .05), The Western Ontario and McMaster Universities Arthritis Index (WOMAC) scores (P > .05), Post-operative nausea and vomiting occurrence (P > .05) regardless of the presence of morphine or not in the injections. CONCLUSION: Additional morphine added to multimodal cocktail did not decrease the postoperative pain scores significantly based on our outcomes, but it reduced the systemic postoperative opioids consumption in total knee arthroplasty.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.019 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".