Validation of a Population-Based Algorithm to Detect Chronic Psychotic Illness
Bibliographic record
Abstract
OBJECTIVE: To validate algorithms to detect people with chronic psychotic illness in population-based health administrative databases. METHOD: We developed 8 algorithms to detect chronic psychotic illness using hospitalization and physician service claims data from administrative health databases in Ontario to identify cases of chronic psychotic illness between 2002 and 2007. Diagnostic data abstracted from the records of 281 randomly selected psychiatric patients from 2 hospitals in Toronto were linked to the administrative data cohort to test sensitivity, specificity, and positive predictive values (PPV) and negative predictive values. RESULTS: Using only hospitalization data to capture chronic psychotic illness yielded the highest specificity (range 69.9% to 84.7%) and the highest PPV (range 55.2% to 80.8%). Using physician service claims in addition to hospitalization data to capture cases increased sensitivity (range 90.1% to 98.8%) but decreased specificity (range 31.1% to 68.0%) and PPV (range 38.4% to 71.1%). CONCLUSION: Using health administrative data to study population-based outcomes for people with chronic psychotic illness is feasible and valid. Researchers can select case identification methods based on whether a more sensitive or more specific definition of chronic psychotic illness is desired.
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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 | 0.014 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".