Does Regional Anesthesia Improve Outcome After Total Knee Arthroplasty?
Bibliographic record
Abstract
Total knee arthroplasty (TKA) is amenable to various regional anesthesia techniques that may improve patient outcome. We sought to answer whether regional anesthesia decreased mortality, cardiovascular morbidity, deep venous thrombosis and pulmonary embolism, blood loss, duration of surgery, pain, opioid-related adverse effects, cognitive defects, and length of stay. We also questioned whether regional anesthesia improved rehabilitation. To do so, we performed a systematic review of the contemporary literature comparing general anesthesia and/or systemic analgesia with regional anesthesia and/or regional analgesia for TKA. To reflect contemporary surgical and anesthetic practice, only randomized, controlled trials from 1990 onward were included. We identified 28 studies involving 1538 patients. There was insufficient evidence from randomized, controlled trials alone to conclude if anesthetic technique influenced mortality, cardiovascular morbidity other than postoperative hypotension, or the incidence of deep venous thrombosis and pulmonary embolism when using thromboprophylaxis. Our review suggests there was no difference in perioperative blood loss or duration of surgery in patients who received general anesthesia versus regional anesthesia. Compared with general anesthesia and/or systemic analgesia, regional anesthesia and/or analgesia reduced postoperative pain, morphine consumption, and opioid-related adverse effects. Length of stay may be reduced and rehabilitation facilitated for patients undergoing regional anesthesia and analgesia for 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".