Validation of classification algorithms for childhood diabetes identified from administrative data
Bibliographic record
Abstract
OBJECTIVE: Type 1 diabetes is the most common form of diabetes among children; however, the proportion of cases of childhood type 2 diabetes is increasing. In Canada, the National Diabetes Surveillance System (NDSS) uses administrative health data to describe trends in the epidemiology of diabetes, but does not specify diabetes type. The objective of this study was to validate algorithms to classify diabetes type in children <20 yr identified using the NDSS methodology. PATIENTS AND METHODS: We applied the NDSS case definition to children living in British Columbia between 1 April 1996 and 31 March 2007. Through an iterative process, four potential classification algorithms were developed based on demographic characteristics and drug-utilization patterns. Each algorithm was then validated against a gold standard clinical database. RESULTS: Algorithms based primarily on an age rule (i.e., age <10 at diagnosis categorized type 1 diabetes) were most sensitive in the identification of type 1 diabetes; algorithms with restrictions on drug utilization (i.e., no prescriptions for insulin ± glucose monitoring strips categorized type 2 diabetes) were most sensitive for identifying type 2 diabetes. One algorithm was identified as having the optimal balance of sensitivity (Sn) and specificity (Sp) for the identification of both type 1 (Sn: 98.6%; Sp: 78.2%; PPV: 97.8%) and type 2 diabetes (Sn: 83.2%; Sp: 97.5%; PPV: 73.7%). CONCLUSIONS: Demographic characteristics in combination with drug-utilization patterns can be used to differentiate diabetes type among cases of pediatric diabetes identified within administrative health databases. Validation of similar algorithms in other regions is warranted.
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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 | 0.034 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".