Bibliographic record
Abstract
OBJECTIVE: To examine changes in prevalence rates of treated schizophrenia over 10 years in a small urban teaching centre using data from the Kingston Psychiatric Record Linkage System (KPRLS). METHOD: The KPRLS, a psychiatric case register established in 1984, collects and links demographic, diagnostic, and service use information for all psychiatric inpatients, outpatients and emergency contacts at the 3 hospitals in Kingston, Ontario. A preliminary comparison of first admissions for schizophrenia used chart review (1976-1978) and KPRLS data (1996-1998). The KPRLS data were used to calculate population-based prevalence rates of treated schizophrenia in 3 census years (1986, 1991, 1996) for patients in the 2 counties closest to Kingston. RESULTS: The preliminary comparison showed a 42% decrease in the number of first-admission schizophrenia cases over 20 years. In the main study, the annual inpatient prevalence rates decreased significantly (52%) from 1986 to 1996 with no corresponding change in outpatient rates, regardless of sex. Although total major affective disorders increased, this was due to an increase in major depression, not bipolar disorder. CONCLUSIONS: This is the first Canadian case-register study to support the widely reported falling rates of schizophrenia in other parts of the world over the last 40 years. Since this is a geographically limited prevalence study based on only 10 years of data, further research over longer periods of time in other regions of the country is required to support or refute these findings.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".