Risk factors associated with readmissions of patients with severe mental disorders under treatment with antipsychotics
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to assess the risk of readmission in patients with severe mental disorders, compare it between patients using different types of antipsychotics and determine risk factors for psychiatric readmission. METHODS: Medical records of a non-concurrent cohort of 625 patients with severe mental disorders (such as psychoses and severe mood disorders) who were first discharged from January to December 2012 (entry into the cohort), with longitudinal follow-up until December 2017 constitute the sample. Descriptive statistical analysis of characteristics of study sample was performed. The risk factors for readmission were assessed using Cox regression. RESULTS: Males represented 51.5% of the cohort, and 75.6% of the patients had no partner. Most patients (89.9%) lived with relatives, and 64.7% did not complete elementary school. Only 17.1% used more than one antipsychotic, 34.2% did not adhere to the treatment, and 13.9% discontinued the medication due to unavailability in public pharmacies. There was a need to change the antipsychotic due to the lack of therapeutic response (11.2% of the patients) and adverse reactions to the antipsychotic (5.3% of the patients). Cox regression showed that the risk of readmission was increased by 25.0% (RR, 1.25; 95% CI, 1.03-1.52) when used typical antipsychotics, compared to those who used atypical ones, and by 92.0% (RR, 1.92; 95% CI, 1.63-2.27) when patients did not adhere to maintenance treatment compared to those who adhered. CONCLUSIONS: Use of atypical antipsychotics and adherence to treatment were associated with a lower risk of psychiatric readmissions.
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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".