Do Patients Follow-Up with Discharge Advice After Psychiatric Assessment and Discharge from Emergency Department?
Bibliographic record
Abstract
Aims: To assess if patients follow-up with discharge advice after psychiatric assessment and discharge from Emergency Department (ED). Method: All patients with psychiatric complaint who presented at three ED's in Edmonton, Alberta, Canada were identified via ED Information System (EDIS). Patients presenting complaint were entered onto the EDIS by Triage Nurse along with demographic information. All charts were reviewed and clinical data was obtained. Patients who were assessed by Psychiatry and discharged home were contacted via telephone to determine if they followed-up with discharge advice of psychiatry team. Results: A total of 1420 patients have been identified during April and May 2008.Chart review has been completed. Data entry and follow-up is in progress. Preliminary data of 250 patients is presented here. 55% were male. Mean age 37 years (SD 12). 47% presented voluntarily. Psychiatry was consulted for 53% of patients. The presenting complaint was “Suicidal Ideation” in 29% and “Bizarre behaviour” in 24%. Primary diagnoses for those seen by Psychiatry were mood disorder (30%) and psychotic disorder (26%). Out of those seen by Psychiatry 36% were admitted. 44% of those admitted by psychiatry were diagnosed with psychotic disorder followed by mood disorder in 31%. The patients who were discharged home by Psychiatry were advised to follow up with their family doctor 7%, psychiatrist 15%, outpatient psychiatry services 16% and addiction services 16%. Conclusion: This is the first report of outcome of discharge advice and will help in developing and planning community follow-up system for psychiatric patients.
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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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".