Impact of Physician Follow-Up Care on Psychiatric Readmission Rates in a Population-Based Sample of Patients With Schizophrenia
Bibliographic record
Abstract
OBJECTIVE: The study evaluated the association between physician follow-up within 30 days after hospital discharge and psychiatric readmission within the subsequent 180 days. METHODS: Among inpatients with schizophrenia who were discharged between 2007 and 2012 in Ontario (N=19,132), those who had a 30-day follow-up visit with a primary care physician (PCP) only, a psychiatrist only, or both were compared with a no-follow-up group. The primary outcome was psychiatric readmission in the subsequent 180 days. Secondary analyses stratified the sample on the basis of readmission risk at discharge. RESULTS: About 65% of patients had follow-up care within 30 days postdischarge. Psychiatric readmission rates were similar among patients with any physician follow-up and significantly lower than among those with no follow-up (26%): PCP only: 22%; adjusted hazard ratio [aHR]=.88, 95% confidence interval [CI]=.81-.96; psychiatrist only, 22%; aHR=.84, CI=.77-.90; both, 21%, aHR=.82, CI=.75-.90). In stratified analyses, 66% of patients were in the category at highest risk of psychiatric readmission, and the effect of follow-up with any physician was significant for these patients, compared with high-readmission risk patients with no follow-up, who had a 29% readmission rate (PCP only, 20% readmission rate, aHR=.85, CI=.77-.94; psychiatrist only, 29%, aHR=.84, CI=.77-.92; both, 17%, aHR=.81, CI=.73-.90). DISCUSSION: Timely physician follow-up was associated with reduced risk of psychiatric readmissions, with the greatest reduction among patients at high risk of readmission. Because more than one-third of patients had no physician visit within 30 days postdischarge, improving physician follow-up may help reduce psychiatric readmission rates.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".