A Bayesian Approach to Latent Class Modeling for Estimating the Prevalence of Schizophrenia Using Administrative Databases
Why this work is in the frame
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Bibliographic record
Abstract
Estimating the incidence and the prevalence of psychotic disorders in the province of Quebec has been the object of some interest in recent years as a contribution to the epidemiological study of the causes of psychotic disorders being carried out primarily in UK and Scandinavia. A number of studies have used administrative data from the Régie de l'assurance maladie du Québec (RAMQ) that includes nearly all Quebec citizens to obtain geographical and temporal prevalence estimates for the illness. However, there has been no investigation of the validity of RAMQ diagnoses for psychotic disorders, and without a measure of the sensitivity and the specificity of these diagnoses, it is impossible to be confident in the accuracy of the estimates obtained. This paper proposes the use of latent class analysis to ascertain the validity of a diagnosis of schizophrenia using RAMQ data.
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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 | 0.001 | 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 | 0.001 | 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