Perioperative outcomes associated with general and spinal anesthesia after total joint arthroplasty for osteoarthritis: a large, Canadian, retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Compared with general anesthesia, spinal anesthesia has many benefits for patients undergoing total hip (THA) or total knee (TKA) arthroplasty, but few studies have explored rates of morbidity and mortality. We aimed to compare perioperative outcomes by anesthetic type for patients undergoing THA or TKA for osteoarthritis. METHODS: We identified patients who underwent primary THA or TKA from the affiliated institute's database. We calculated inpatient, 30-day, 60-day and 90-day mortality rates, as well as 90-day perioperative complications, readmissions and length of stay (LOS). We compared outcomes between groups using logistic regression and propensity-adjusted multivariate analysis. RESULTS: We included 6100 (52.2%) patients undergoing THA and 5580 (47.8%) undergoing TKA. We found no differences by anesthetic type in mortality rates up to 90 days after surgery. Patients under spinal anesthesia were less likely to need a blood transfusion (THA odds ratio [OR] 0.75, 95% confidence interval [CI] 0.60 to 0.92; TKA OR 0.52, 95% CI 0.40 to 0.67) and were more likely to be discharged home among those who underwent TKA (OR 1.61, 95% CI 1.30 to 2.00). Patients who received spinal anesthesia for THA had a longer LOS (0.28 d, 95% CI 0.17 to 0.39), and patients who received spinal anesthesia for TKA had a shorter LOS than those who received general anesthesia (-0.34 d, 95% CI -0.51 to -0.18). Anesthetic type was not associated with any difference in adverse events. CONCLUSION: These findings may inform decisions on anesthetic type for total joint arthroplasty, especially for rapid discharge protocols. Further research is needed to understand postoperative pain and functional outcomes between anesthetic types.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".