A Comparison of Medical and Psychobehavioral Emergency Department Visits Made by Adults with Intellectual Disabilities
Bibliographic record
Abstract
Study Objective. We describe and contrast medical to psychobehavioral emergency visits made by a cohort of adults with intellectual disabilities. Methods. This was a study of 221 patients with intellectual disabilities who visited the emergency department because of a psychobehavioral or medical emergency. Patient profiles are described and logistic regression was used to assess predictors of psychobehavioral emergencies in this group, including age, residence, psychiatric diagnosis, cognitive level, and life events. Results. Ninety-eight individuals had medical emergencies and 123 individuals presented with psychobehavioral emergencies. The most common medical issue was injury and the most common psychobehavioral issue was aggression. In the multivariate analysis, life events (odds ratio (OR) 0.28; 95% confidence interval (CI) 0.10 to 0.75), psychiatric diagnosis (OR 2.35; 95% CI 1.12 to 4.95), and age group (OR 4.97; 95% CI 1.28 to 19.38) were associated with psychobehavioral emergencies. Psychobehavioral emergencies were more likely to result in admission and caregivers reported lower rates of satisfaction with these visits. Conclusion. Emergency departments would benefit from greater understanding of the different types of presentations made by adults with intellectual disabilities, given the unique presentations and outcomes associated with them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".