Patient-, Hospital-, and System-Level Factors Associated With 30-Day Readmission After a Psychiatric Hospitalization
Bibliographic record
Abstract
ABSTRACT: Readmission after inpatient care for a psychiatric condition is associated with a range of adverse events including suicide and all-cause mortality. This study estimated 30-day readmission rates in a large cohort of inpatient psychiatric admissions in New York State and examined how these rates varied by patient, hospital, and service system characteristics. Data were obtained from Medicaid claims records, and clinician, hospital, and region data, for individuals with a diagnosis of any mental disorder admitted to psychiatric inpatient units in New York State from 2012 to 2013. Psychiatric readmission was defined as any unplanned inpatient stay with a mental health diagnosis with an admission date within 30 days of being discharged. Unadjusted and adjusted odds ratios of being readmitted within 30 days were estimated using logistic regression analyses. Over 15% of individuals discharged from inpatient units between 2012 and 2013 were readmitted within 30 days. Patients who were readmitted were more likely to be homeless, have a schizoaffective disorder or schizophrenia, and have medical comorbidity. Readmission rates varied in this cohort mainly because of individual-level characteristics. Homeless patients were at the highest risk of being readmitted after discharge.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".