Assessing administrative databases for surveillance of depressive disorders in Newfoundland and Labrador
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 (EMR) at three family practice clinics in St. John's were audited based on a sample of patients classified as being diagnosed or not diagnosed with a depressive disorder. The EMR served as the gold standard, which was then 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 disorders diagnoses (either in hospital or physician claims data), date of diagnosis, and service providers type (general practitioners vs. psychiatrists). This study found that provincial hospital and physician databases are useful for carrying out surveillance on depressive disorders using certain types of case definitions. The inclusion of medications dispensed to patients was found to increase the sensitivity and decrease the false negative rate for certain case definitions. The availability of prescription data from population-based Pharmacy Networks in the development of case definitions presents an opportunity for the development of more advanced surveillance methods for depressive disorders in Canada.
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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 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".