Anesthesia Technique and Mortality after Total Hip or Knee Arthroplasty
Bibliographic record
Abstract
BACKGROUND: This propensity score-matched cohort study evaluates the effect of anesthetic technique on a 30-day mortality after total hip or knee arthroplasty. METHODS: All patients who had hip or knee arthroplasty between January 1, 2003, and December 31, 2014, were evaluated. The principal exposure was spinal versus general anesthesia. The primary outcome was 30-day mortality. Secondary outcomes were (1) perioperative myocardial infarction; (2) a composite of major adverse cardiac events that includes cardiac arrest, myocardial infarction, or newly diagnosed arrhythmia; (3) pulmonary embolism; (4) major blood loss; (5) hospital length of stay; and (6) operating room procedure time. A propensity score-matched-pair analysis was performed using a nonparsimonious logistic regression model of regional anesthetic use. RESULTS: We identified 10,868 patients, of whom 8,553 had spinal anesthesia and 2,315 had general anesthesia. Ninety-two percent (n = 2,135) of the patients who had general anesthesia were matched to similar patients who did not have general anesthesia. In the matched cohort, the 30-day mortality rate was 0.19% (n = 4) in the spinal anesthesia group and 0.8% (n = 17) in the general anesthesia group (risk ratio, 0.42; 95% CI, 0.21 to 0.83; P = 0.0045). Spinal anesthesia was also associated with a shorter hospital length of stay (5.7 vs. 6.6 days; P < 0.001). CONCLUSIONS: The results of this observational, propensity score-matched cohort study suggest a strong association between spinal anesthesia and lower 30-day mortality, as well as a shorter hospital length of stay, after elective joint replacement surgery.
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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 | 0.004 |
| 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 | 0.000 | 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".