At-a-glance - Twenty years of diabetes surveillance using the Canadian Chronic Disease Surveillance System
Bibliographic record
Abstract
In 1999, the Government of Canada, along with the provinces and territories, established the National Diabetes Surveillance System (NDSS) to track rates of diabetes in Canada. The NDSS used a novel method to systematically collect and report national diabetes data using linked administrative health databases. The NDSS has since evolved to become the Canadian Chronic Disease Surveillance System (CCDSS) and provides information on over 20 chronic conditions. This At-a-glance report provides the most up-to-date CCDSS information on diabetes rates in Canada. Currently, 8.8% of Canadians (9.4% male, 8.1% female, aged one year and older) live with diabetes, and approximately 549 new cases are diagnosed each day. Since 2000, the age-standardized prevalence rate has increased by an average of 3.3% per year. The age-standardized incidence rate has remained relatively stable, and all-cause mortality rates among those with diabetes have decreased by an average of 2.1% per year. This suggests that people are living longer with a diabetes diagnosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".