Movement if Life—Optimizing Patient Access to Total Joint Arthroplasty: Alcohol and Substance Abuse Disparities
Bibliographic record
Abstract
Alcohol use disorders (AUDs) and substance use disorders (SUDs) place patients undergoing total joint arthroplasty at notable risk for complications. AUD and SUD disproportionately affect vulnerable communities and often coexist. Following is a discussion of the presence of these disorders in vulnerable populations and approaches to screening for them to optimize care and reduce the risks of joint arthroplasty surgery. 25.1% of American adults report binge drinking in the past year, and 5.8% of American adults carry a diagnosis of AUD. Alcohol consumption and AUD disproportionately affect American Indians/Alaskan Natives, and heavy episodic drinking is highest in Latinx and American Indians. AUD is higher in those who are unemployed, have lower education level, and those who are single/divorced. Alcohol use in the preoperative period is associated with difficulty maintaining blood pressure during surgery, infections, wound disruptions, and increased length of stay. In addition, patients with AUD or unhealthy alcohol use have a greater comorbidity burden, including liver disease and dementia, that predisposes them to poor surgical outcomes. Optimization in these vulnerable populations include proper screening, cessation programs, psychosocial interventions, assessment of support systems, and pharmacologic interventions. 38% of adults battle a drug use disorder. Twenty-one million Americans have at least one addiction, but only 10% receive treatment. Rates of opioid use and opioid-related deaths have continued to rise. Recreational drug use is highest in American Indians. Marijuana use is highest in Black and Latinx lesbian, gay, and bisexual women. Overall, substance use is associated with depression and anxiety; discrimination based on race, ethnicity, sex, or sexual preference is also deeply interwoven with depression, anxiety, and substance use. Preoperative use of opioids is the number one predictor of prolonged chronic postoperative opioid use. Optimization in these vulnerable groups begins with appropriate screening, followed by psychosocial interventions, social work and substance abuse counseling, and pharmacologic therapies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 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".