An algorithm to differentiate diabetic respondents in the Canadian Community Health Survey.
Bibliographic record
Abstract
OBJECTIVES: This article describes an algorithm to classify respondents to cycle 1.1 (2000/2001) of the Canadian Community Health Survey (CCHS) according to whether they have type 1, type 2 or gestational diabetes. DATA SOURCE: The data are from the chronic disease module and the drug module of cycle 1.1 of the CCHS. ANALYTICAL TECHNIQUES: A total of 6,361 respondents to cycle 1.1 of the CCHS reported that a health care professional had diagnosed them as having diabetes. The Ng-Dasgupta-Johnson algorithm classifies this group according to whether they have type 1, type 2 or gestational diabetes, based on their answers to CCHS questions about diabetes during pregnancy, use of oral medications to control diabetes, use of insulin, timing of initiation of insulin treatment, and age at diagnosis. MAIN RESULTS: Application of an earlier algorithm to CCHS cycle 1.1 results in a 10%-90% split for type 1 and type 2 diabetes. By contrast, the Ng-Dasgupta-Johnson algorithm yields a 5%-95% split. This is not unreasonable, given the rapid rise in obesity, a major risk factor for type 2 diabetes, in Canada.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".