Early Postoperative Mortality Following Joint Arthroplasty: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To perform a systematic review of 30- and 90-day mortality rates in patients undergoing hip or knee arthroplasties. METHODS: Five databases were searched for English-language studies of mortality in hip or knee arthroplasties and the following data were extracted: patient characteristics (age, sex, ethnicity), arthroplasty characteristics (unilateral vs bilateral, hip vs knee), system factors (hospital volume and surgeon volume), year of study, etc. Mortality rates were compared across variable categories; proportions were compared using relative risk ratios and 95% confidence intervals. RESULTS: Out of 650 titles and abstracts, 80 studies qualified for analysis. Of these, 35%, 34%, and 31% of studies provided 30-, 90-, and > 90-day mortality rates. Overall 30-day mortality rates across all types of arthroplasties were 0.3%; 90-day, 0.7%. For those reports with specific rates, 30-day mortality was significantly higher in men than women [1.8% vs 0.4%, respectively; relative risk (RR) 3.93, 95% CI 3.30-4.68] and in bilateral versus unilateral procedures (0.5% vs 0.3%; RR 1.6, 95% CI 1.49-1.72), but no differences were noted by the underlying diagnosis of osteoarthritis (OA) versus rheumatoid arthritis (0.4% vs 0.3%; RR 0.77, 95% CI 0.48-1.24). 90-day mortality showed nonsignificant trends favoring women, OA as the underlying diagnosis, and unilateral procedures. CONCLUSION: Several demographic and surgical factors were associated with higher 30-day mortality rates following knee and hip arthroplasties. More studies are needed to examine the effect of body mass index, comorbidities, and other modifiable factors, in order to identify interventions to lower mortality rates following arthroplasty procedures.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".