Considering a risk profile based on emergency department utilization in young people with eating disorders: Implications for early detection
Bibliographic record
Abstract
OBJECTIVE: While screening tools are available for the early identification of eating disorders, it may not be feasible to employ them in an emergency department (ED). Establishing a risk profile may improve the screening process. The purpose of this study was to investigate ED service utilization among patients with eating disorders and create a risk profile to help detect eating disorders at an earlier and more treatable stage. METHOD: We applied a concurrent mixed methods research design, however, only the quantitative findings will be presented. Our study involved a retrospective cohort analysis of administrative ED health data for patients (n = 243) aged 12-24 years in an eating disorders program. Two control groups: (1) all-cause (n = 716), (2) and mental health (n = 679) were included. RESULTS: 68.7% of eating disorder patients were discharged from the ED without follow-up being arranged. Comorbidities were recorded as the primary or secondary diagnosis, and patients presented with suicidality more frequently than controls (χ = 31.2, p < .001). Patients accessed ED services five times more often than controls. DISCUSSION: Despite eating disorder patients accessing the ED more frequently than controls, eating disorder diagnoses were not always assigned or documented. Our findings highlight the importance of enhanced eating disorder training for ED health care staff to better understand the risk profile, and the consideration of comorbidities and suicide risk when assessing patients to ensure early detection. CONCLUSION: As eating disorders are often undetected, more comprehensive training and access to screening tools may help improve detection, mitigate symptom progression, and enhance patient safety.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".