Caring for individuals with eating disorders–how to improve care while reducing unnecessary spending?
Bibliographic record
Abstract
Eating disorders are mental health conditions defined by abnormal eating behaviours that negatively affect a person's physical and/or mental health.Eating disorders typically include anorexia nervosa, bulimia nervosa, binge eating disorder, and other 'specified feeding or eating disorders'that do not meet the strict diagnostic criteria of the aforementioned conditions.These disorders have a combined lifetime prevalence of about 7% in women and 3% in men [1] and are frequently associated with psychiatric comorbidities, such as mood and anxiety disorders, post-traumatic stress disorder, and substance use disorders [2], often requiring longterm treatment [3].Eating disorders can also cause short-and long-term medical complications [4], such as cardiovascular and renal problems, gastrointestinal disturbances, fluid and electrolyte abnormalities, menstrual and fertility problems (among females), osteoporosis and osteopenia, and dental and dermatological problems [5,6].Moreover, anorexia nervosa has the highest mortality rate of any psychiatric disorder [7].Previous studies have shown that the economic burden of eating disorders is substantial [8][9][10].Due to the high costs of care in this population, well-organised efforts directed toward early intervention and active management of these individuals' physical and mental health are warranted.Furthermore, given the surge in eating disorders-related emergency department visits and medical hospitalizations (i.e., acute care) among young women in Canada throughout the pandemic [11], it is important to understand whether there are ways to improve care among this particular population.Many jurisdictions have implemented strategies, such as high-risk care management, to reduce costs and improve the quality of care among patients with high health care needs.High-risk care management involves the provision of intensive, one-on-one services by a health worker, such as a nurse, to patients with complex needs, such as those with an eating disorder.The idea behind these types of strategies/interventions is that the implementation of high-quality outpatient care may help reduce unnecessary acute care for these patients.For example, research suggests that stepped care models, where primary care clinicians play a greater role in service delivery, may be an option to improve patient outcomes in a cost-effective manner [12].However, it is unclear whether any costs can be reduced, and if so which, especially among patients who require costly care. How to reduce unnecessary health care spending?One potential way to decrease health care spending, without sacrificing high-quality care, may be to target preventable (i.e., potentially unnecessary) acute care among patients with high
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".