The impact of acute perioperative myocardial infarction on clinical outcomes after total joint replacement
Bibliographic record
Abstract
Background: Improvements in perioperative care have markedly decreased mortality after total joint replacement. Acute myocardial infarct (MI) is the most common clinically significant complication after total joint replacement (TJR) and the most common cause of 30-day mortality after TJR, which remains a concern especially in light of an older population with advanced comorbidities. In spite of this, little evidence exists in regard to its effect on TJR functional outcomes. Methods: To assess the potential impact, if any, of acute MI on the clinical outcomes of patients undergoing primary TJR, a matched cohort study of MI and non-MI patients was conducted to determine 1-year Oxford, Harris Hip and Knee Society score outcomes. Results: Of 12,739 primary TJR patients identified over a 9-year period, 0.9% (114; 95% CI, 0.75-1.1) experienced a perioperative MI. A greater proportion of MI than non-MI patients had ≥1 cardiac risk factor (P=0.001) and an American Society for Anesthesiologist (ASA) 4 status (P=0.037). Length of hospital stay was longer for MI cases (MI=11.5±9.8 vs. Non-MI=5.4±2.7, P<0.0001), with 70% requiring intensive care unit or cardiac care unit stays (P<0.0001). One-year outcome scores were similar among groups (P>0.05). One-year cardiac mortality rate was 6.1% compared to 0 non-MI deaths (P<0.0001). Conclusions: While functional outcomes of MI after TJR are equivalent to non-MI, 1-year mortality remains high, and targeted cardiac screening and long-term monitoring for this patient population should be implemented.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".