Outcome of Spinal Versus General Anesthesia in Revision Total Hip Arthroplasty: A Propensity Score-Matched Cohort Analysis
Bibliographic record
Abstract
INTRODUCTION: Spinal anesthesia has been previously shown to offer improved patient outcomes compared with general anesthesia in revision total knee arthroplasty. This study aimed to evaluate the potential differences in perioperartive and postoperative outcomes in revision total hip arthroplasty (THA) between spinal or general anesthesia. METHODS: A total of 2,656 consecutive patients who underwent revision THA were evaluated. Propensity-score-adjusted multivariate logistic regression analyses were applied to control for intergroup variability and evaluate the differences in outcomes and complications with anesthesia type. RESULTS: Propensity score matching resulted in 1:1 matching with 265 patients in each anesthesia cohort. Multivariate analyses demonstrated that patients administered general anesthesia had a significantly longer procedure time (174.8 versus 161.3, P < 0.01), higher intraoperative (402.6 versus 305.5 mL, P < 0.01), and total perioperative blood loss (1802.2 versus 1,684.2 mL,P < 0.01). In addition, patients administered general anesthesia were found to have higher odds for two or more inhospital complications (odds ratio, 4.51, P < 0.01) and extended length of stay (odds ratio, 2.45, P = 0.02). DISCUSSION: Our study shows that propensity-matched patients who received spinal anesthesia for revision THA exhibited notable reduction in surgical time, perioperative blood loss, and complications compared with patients who received general anesthesia, suggesting that spinal anesthesia is a viable alternative to general anesthesia in revision THA.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".