DEFINING PEDIATRIC DIABETES USING EMR RECORDS AND VALIDATION FROM LINKABLE MANITOBA COHORT DATA
Bibliographic record
Abstract
Abstract BACKGROUND The prevalence of paediatric diabetes is increasing. Identifying and describing populations with paediatric diabetes using Primary Care Electronic Medication Records (EMR) can improve surveillance and management. OBJECTIVES To describe the population of children diagnosed with paediatric diabetes in Manitoba using Electronic Medical Record data from Community Paediatricians and Family Physcians in Manitoba. DESIGN/METHODS We applied a previously validated case definition for type 1 and type 2 diabetes to patients aged 1–18 seen by one of the 221 primary care providers participating in the Manitoba Primary Care Research Network (MaPCReN) between 1998–2015. We compared the agreement between the MaPCReN definition and Manitoba’s Diabetes Education Resource for Children and Adolescents (DERCA) clinical database of confirmed cases. Cases were described, including prevalence, patient characteristics, and health system use. RESULTS Our definition identified 166 children (0.4%, 95% CI 0.36% - 0.49%) of whom 53.0% lived in a rural location and 53.6% were female. The mean age at diagnosis was 11.4 years (SD 5.4). There were 90 patients identified by the definition also cared for by a paediatric endocrinologist at DERCA [sensitivity (54.2%), specificity (98.7%), and kappa (0.61, CI 0.54-.069)]. An additional 286 patients had at least one documented HbA1C of 6.5% or higher but did not have a corresponding diabetes diagnosis within the EMR. Of those, 45% had an HbA1c between 6.5 -7.5 and 25.9% had an HbA1c over 8.5%. Most of these patients also had an abnormal fasting glucose in the EMR (76.9%). There were 280 patients with an elevated HbA1c that had no evidence of attending an appointment with a paediatric endocrinologist at DERCA, 70.8% have a rural address. CONCLUSION The inclusion of HbA1c values in identifying paediatric diabetes suggested a large number of patients without a corresponding diabetes diagnosis or record of care from DERCA. Therefore, the DERCA database might be underestimating the true prevalence of diabetes in Manitoba. Understanding further characteristics of this population, is an essential step to inform the development of enhanced services and strategies.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".