Orthopaedic Nurse Navigators and Total Joint Arthroplasty Preoperative Optimization
Bibliographic record
Abstract
Diabetes and cardiovascular disease are some of the most common risk factors for complications after total joint arthroplasty (TJA). Preoperative optimization programs are dependent on nurse navigators for coordination of interventions that improve patients' health and surgical outcomes. This article uses information regarding the current practices for diabetes and cardiovascular disease management to provide recommendations for nurse navigators when managing these risk factors prior to TJA. We consulted nurse navigators and conducted a literature review to learn about strategies for addressing diabetes and cardiovascular disease in preoperative optimization programs. Nurse navigators can play a critical role in addressing these conditions by providing patient education and implementing preoperative optimization protocols that incorporate discussion regarding guidelines for diabetes and cardiovascular disease management prior to surgery. This article shares recommendations and resources for nurse navigators to help address diabetes and cardiovascular disease as part of preoperative optimization programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".