Faculty Opinions recommendation of Anesthetic management and surgical site infections in total hip or knee replacement: a population-based study.
Bibliographic record
Abstract
BACKGROUND: Epidural or spinal anesthesia involves several mechanisms hypothesized to reduce risk of surgical site infections (SSIs) during this decisive period. This study aims to compare the risk of SSI within 30 days of surgery for patients receiving total hip or knee replacement under general anesthesia versus those under epidural or spinal anesthesia.METHODS: We used the Longitudinal Health Insurance Database of Taiwan. A total of 3,081 patients who underwent primary total hip or knee replacement from 2002 to 2006 were included in the study. Multivariate logistic regression and propensity score analyses were carried out to explore the relationship between method of surgical anesthesia and SSI occurring within 30 days of surgery.RESULTS: Of the 3,081 sampled patients, 56 patients (1.8%) had 30-day SSIs; 33 (2.8% of all under general anesthesia) of them had general anesthesia, and 23 (1.2% of all under epidural or spinal anesthesia) had epidural or spinal anesthesia (P = 0.002). The odds of SSI for patients receiving total hip or knee replacement under general anesthesia were 2.21 (95% CI = 1.25-3.90, P = 0.007) times higher than those who had the same procedure under epidural or spinal anesthesia, after adjusting for the patient's age, sex, the year of surgery, comorbidities, surgeon's age, and hospital teaching status.CONCLUSIONS: Total hip or knee replacement under general anesthesia is associated with higher risk of SSI compared with epidural or spinal anesthesia. Our results support the evolving concept of long-term consequences of anesthesia and emphasize the anesthesiologist's role in preventing SSIs. PMID: 20657202
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.006 |
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".