Movement Is Life—Optimizing Patient Access to Total Joint Arthroplasty: Diabetes Mellitus Disparities
Bibliographic record
Abstract
This is one of a series of articles that focuses on maximizing access to total joint arthroplasty by providing preoperative optimization pathways to all patients to promote the best results and minimize postoperative complications. Because of inequities in health care, an optimization process that is not equipped to support the underserved can potentially worsen disparities in the utilization of arthroplasty. A staggering 10.5% of the American population lives with diabetes mellitus. Diabetes prevalence is 17% higher in rural communities compared with urban communities. Rates of diabetes are higher in African American, Hispanic, and American Indian populations. Barriers to health care are higher in rural areas and for vulnerable communities, positioning the management of diabetes at the intersection of risk. Poor glycemic control is a predictor of periprosthetic joint infection. Optimization tools include assessing for food security, knowledge of a social safety net and community resources, patient diabetic literacy, and relationships with primary care providers to ensure continuous check-ins as well as partnering with specialty endocrine diabetic clinics. Several strategic recommendations, such as healthcare navigators and promotores (Latinx population), are made to enable and empower, such as continuous glucose monitoring, the preoperative patient to reach a safe preoperative optimization goal for their TJA surgery.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".