Orthopaedic Nurse Navigators and Total Joint Arthroplasty Preoperative Optimization
Bibliographic record
Abstract
Preoperative optimization programs for total joint arthroplasty identify and address risk factors to reduce postoperative complications, thereby improving patients' ability to be safe surgical candidates. This article introduces preoperative optimization programs and describes the role of orthopaedic nurse navigators. This foundation will be used to produce an article series with recommendations for optimization of several modifiable biopsychosocial factors. We consulted orthopaedic nurse navigators across the United States and conducted a literature review regarding preoperative optimization to establish the importance of nurse navigation in preoperative optimization. The responsibilities of nurse navigators, cited resources, and structure of preoperative optimization programs varied among institutions. Optimization programs relying on nurse navigators frequently demonstrated improved outcomes. Our discussions and literature review demonstrated the integral role of nurse navigators in preoperative optimization. We will discuss specific risk factors and how nurse navigators can manage them throughout this article series.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".