Characterization of Older Emergency Department Patients Admitted to Psychiatric Units
Bibliographic record
Abstract
BACKGROUND: Many older patients presenting to emergency departments (EDs) with psychiatric complaints require admission to geropsychiatric units (GPUs). The medical evaluation needed prior to this is not understood. Our goal was to understand ED evaluation practices for patients admitted to the GPU through the ED and understand the medical problems identified after admission. METHODS: Via retrospective chart review, we abstracted demographics, medical history, ED complaint, evaluation, length of stay, and diagnosis. The number of patients later transferred from the GPU and the reasons for such transfers were also recorded. RESULTS: Of 100 patients reviewed, the average age was 78 years. Admission diagnoses were agitation/mania (30%), depression/suicidal ideation (28%), change in mental status/confusion (12%) and other (30%). Most had at least one prior psychiatric and medical diagnosis (77%, 60%). Common ED tests ordered were basic metabolic panel (BMP) (96%), complete blood count (CBC) (94%), urinalysis (UA) (89%), electrocardiogram (EKG) (69%), alcohol level (62%), urine toxicology (61%), chest X-ray (51%), and CT scan of the head (71%). Abnormal findings included urinalysis (24.7%), CBC (23.4%), toxicology (23%), BMP (21.9%), head CT (21.1%), chest X-ray (13.7%), ECG changes (10.1%), and alcohol (4.8%). Five of the 100 GPU admissions were later transferred to a medical floor. CONCLUSION: Most GPU admissions have previous psychiatric and medical issues and are admitted for agitation/mania or depression/suicidal ideation. A certain percentage of patients are transferred out due to medical issues despite ED evaluation. However, it is unlikely that further ED testing would reduce this percentage. Further research of medical screening for geropsychiatric patients may elucidate ideal medical clearance procedures.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".