Validation of a Pediatric Diabetes Case Definition Using Administrative Health Data in Manitoba, Canada
Bibliographic record
Abstract
OBJECTIVE: To validate a case definition for diabetes in the pediatric age-group using administrative health data. RESEARCH DESIGN AND METHODS: Population-based administrative data from Manitoba, Canada for the years 2004-2006 were anonymously linked to a clinical registry to evaluate the validity of algorithms based on a combination of hospital claim, outpatient physician visit, and drug use data over 1-3 years in youth 1-18 years of age. Agreement between data sources, sensitivity, specificity, negative (NPV) and positive predictive value (PPV) were evaluated for each algorithm. In addition, ascertainment rate of each data source, prevalence, and differences between subtypes of diabetes were evaluated. RESULTS: Agreement between data sources was very good. The diabetes definition including one or more hospitalizations or two or more outpatient claims over 2 years provided a sensitivity of 94.2%, specificity of 99.9%, PPV of 81.6% and NPV of 99.9%. The addition of one or more prescription claims to the same definition over 1 year provided similar results. Case ascertainment rates of both sources were very good to excellent and the ascertainment-corrected prevalence for youth-onset diabetes for the year 2006 was 2.4 per 1,000. It was not possible to distinguish between subtypes of diabetes within the administrative database; however, this limitation could be overcome with an anonymous linkage to the clinical registry. CONCLUSIONS: Administrative data are a valid source for the determination of pediatric diabetes prevalence that can provide important information for health care planning and evaluation.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".