Schizophrenia hospitalizations - a big data approach
Bibliographic record
Abstract
Introduction Schizophrenia is characterized by long hospitalizations and a recurrent use of chronic and acute psychiatric care. Objectives The aim of this study was to analyze schizophrenia related hospitalizations in Portugal. Methods A retrospective observational study was conducted using a nationwide hospitalization database containing all hospitalizations registered in Portuguese public hospitals from 2008 to 2015.Hospitalizations with a primary diagnosis of schizophrenia were selected and schizophrenia subtypes were grouped using the International Classification of Diseases version 9, Clinical Modification(ICD-9-CM) codes of diagnosis 295.xx. Results There was a total of 25,385 hospitalizations in public hospitals of Portugal between 2008 and 2015 with a primary diagnosis of Schizophrenia or other psychotic disorders. A total of 14,279 patients were hospitalized during the study period with an average of 1,78 hospitalizations episodes per patient in the 8-year interval(0.22 hospitalizations/patient/year). 68.0% of the hospitalizations occurred in male patients and the median length of stay was 18.0 days. Mean hospitalization charges were 3,509.7€ per hospitalization, summed to a total charge of 89.1M€. Throughout the study period there was a significant linear decrease in the number of hospitalizations (r = 0.940; B= -47.488; p = 0.001). The last year of the study(2015) had the lowest number of hospitalizations with a total of 2,958 (vs. 3,314 in 2008). When adjusted for the yearly population, there was also a decrease of the number of hospitalizations per 100,000 inhabitants from 31.39 to 28.56 hospitalizations per 100,000 inhabitants between 2008 and 2015, respectively. Conclusions We found differences in hospitalization characteristics by gender, age and primary diagnosis. Disclosure No significant relationships.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".