Multimodal Analgesia for Accelerated Rehabilitation after Total Knee Arthroplasty: A Randomized, Double-Blind, Controlled Trial on the Effect of the Co-Application of Local Infiltration Analgesia and Femoral Nerve Block Combined with Dexmedetomidine
Bibliographic record
Abstract
Background: Multimodal postoperative pain regimens are widely used following total knee arthroplasty (TKA). However, there are few studies on the rehabilitation of the co-application of local infiltration analgesia (LIA) and femoral nerve block (FNB) combined with dexmedetomidine (DEX) for patients undergoing TKA. This study aimed to investigate the effect of LIA plus FNB and co-application of perioperative DEX on TKA outcomes. Methods: 95 patients were randomized into two groups. Patients in group B (n = 48) received a single preoperative FNB and LIA. Patients in group A (n = 47) received FNB and LIA, as well as continuous intravenous injection of DEX starting from the induction of anesthesia to postoperative day 2. All patients were allowed patient-controlled analgesia postoperatively. Visual analog scale (VAS) scores, knee range of motion (ROM) degrees, narcotic consumption, length of hospital stay (LOS), complications, Hospital for Special Surgery (HSS) scores and Montreal Cognitive Assessment-Basic (MoCA-B) Scores were recorded. Results: In group A, the mean VAS scores at rest and during movement were lower, the amount of rescue analgesia was decreased, first time of ambulation was reduced, ROM was improved, MoCA-B Scores were increased, LOS was shorter, HSS scores were higher postoperatively compared with group B (all p < 0.05). Conclusion: Our study indicated multimodal analgesia involving a single FNB and LIA combined with DEX accelerates rehabilitation for patients undergoing TKA.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".