Using Administrative Databases in the Surveillance of Depressive Disorders—Case Definitions
Bibliographic record
Abstract
The objective of this study was to assess the usefulness of provincial administrative databases in carrying out surveillance on depressive disorders. Electronic medical records (EMRs) at 3 family practice clinics in St. John's, NL, Canada, were audited; 253 depressive disorder cases and 257 patients not diagnosed with a depressive disorder were selected. The EMR served as the "gold standard," which then was compared to these same patients investigated through the use of various case definitions applied against the provincial hospital and physician administrative databases. Variables used in the development of the case definitions were depressive disorder diagnoses (either in hospital or physician claims data), date of diagnosis, and service provider type [general practitioner (GP) vs. psychiatrist]. Of the 120 case definitions investigated, 26 were found to have a kappa statistic greater than 0.6, of which 5 case definitions were considered the most appropriate for surveillance of depressive disorders. Of the 5 definitions, the following case definition, with a 77.5% sensitivity and 93% specificity, was found to be the most valid ([ ≥1 hospitalizations OR ≥1 psychiatrist visit related to depressive disorders any time] OR ≥2 GP visits related to depressive disorders within the first 2 years of diagnosis). This study found that provincial administrative databases may be useful for carrying out surveillance on depressive disorders among the adult population. The approach used in this study was simple and resulted in rather reasonable sensitivity and specificity.
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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 | 0.115 | 0.226 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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 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".