Preoperative Patient Optimization for Lower Extremity Total Joint Arthroplasty Surgery
Bibliographic record
Abstract
» Identifying medical comorbidities and optimizing modifiable risk factors (biological, social, and psychological) have been suggested as a strategy to improve the value of total joint arthroplasty (TJA) care, while reducing the risk of intraoperative and postoperative complications. Modifiable biological factors include weight management to reduce obesity, optimizing diabetic control, improving malnutrition, optimizing bone health, improving anemia, managing anticoagulants and bleeding risk, controlling inflammatory conditions, reducing methicillin-sensitive Staphylococcus aureus/methicillin-resistant S. aureus colonization, and reducing frailty. Modifiable social and psychological factors include tobacco and smoking cessation, reducing alcohol use, ceasing drug use/misuse, optimizing mental health (i.e., depression, anxiety), patient TJA education and managing expectations, and evaluating discharge determination and living status. This review comprehensively evaluates and summarizes preoperative patient optimization strategies for lower extremity TJA surgery, both in the primary and revision settings.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".