A model for non-communicable disease surveillance in Canada: the prairie pilot diabetes surveillance system.
Bibliographic record
Abstract
The Prairie Pilot Diabetes Surveillance Project was organized to design and test a prototype population-based surveillance system, using administrative data, for a chronic disease exemplar - diabetes mellitus. The Canadian model of a public health surveillance system for chronic conditions described here specifies a process by which administrative and claims data arising from provincial health insurance programs are merged into an annual person-level summary file (APLSF), yielding one summary record for each person insured within each province. The APLSF is the basis for a variety of estimates, including incidence, prevalence, mortality, complication rates and health services utilization. The model was used to produce comparable interprovincial estimates of several parameters with respect to diabetes for the entire population in the provinces of Alberta, Manitoba and Saskatchewan. All processing of identifiable health data occurred within the provinces where the data were generated. Combining results across provinces was based on further aggregation of the summary data from each province and not by pooling of identifiable person-level data. On the basis of preliminary outputs for diabetes mellitus, the model appears to provide coherent estimates of key diabetes parameters and reflects anticipated differences in health services and outcomes, by disease state. Three characteristics of the model recommend it as a resource for non-communicable disease surveillance in Canada: a) it maximizes the utility of existing data; b) it includes both those with and those without the disease in question; and c) it respects provincial legislation regarding personal health data, yet permits reporting of multi-provincial, population-based data.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".