Brief Communication Hospitalization in the First Year of Treatment for Schizophrenia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objective: To determine the rates of hospitalization during the first year of treatment for schizophrenia, using an epidemiologic sample. Method: We examined inpatient and outpatient administrative databases in the province of Nova Scotia for cases of schizophrenia (ICD-9 code 295 or 298) newly diagnosed during the years 1995 to 1998. We noted the diagnosis site (that is, inpatient or outpatient) and hospitalizations in the year following diagnosis. We also established links to the clinical database maintained by the Nova Scotia Early Psychosis Program (NSEPP). Results: Over the 4-year period, we identified 434 unique cases from an at-risk population of 320 000 (yielding a yearly average age-specific incidence rate of 3.3/10 000), of whom 119 had received care from the NSEPP. Of the cases, 54 % were initially diagnosed while they were inpatients. In the year following diagnosis, the overall hospitalization rate, excluding initial hospitalizations, was 17%. Patients who were initially diagnosed while inpatients had a higher rate of hospitalization in the first year of treatment (25 % vs 7%), compared with those initially diagnosed while outpatients. This relation was also present among patients who received care from the NSEPP. Conclusions: Of newly diagnosed patients with schizophrenia, 46 % were not hospitalized at the time of initial diagnosis. Of all patients, 17 % required hospitalization during the first year of treatment, excluding an initial hospitalization, if present. Hospitalization rates in the first year were higher among patients initially hospitalized and among those with a rural residence. Patients requiring hospitalization during the first year form an important target group for improved interventions. (Can J Psychiatry 2004;49:635–638) Information on funding and support and author affiliations appears at the end of the article.
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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 it