The accuracy of using integrated electronic health care data to identify patients with undiagnosed diabetes mellitus
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: Diabetes mellitus is a growing health and economic burden. Identification of patients with unrecognized diabetes, or those at high risk for diabetes, provides an opportunity for timely intervention. This study assessed the accuracy of using electronic health care data to identify patients with undiagnosed diabetes. METHODS: The study was conducted at a tertiary-care teaching facility in Ottawa, Canada. The study cohort was a stratified random sample of hospitalizations between 1 January 2003 and 31 December 2008. We used diagnostic codes, pharmacy orders and serum glucose tests to classify patients into six groups: 'recognized diabetes' (a diabetes diagnostic code or any diabetes medication), 'probable diabetes' (maximum glucose ≥ 11.1 mmol L(-1)), 'possible diabetes' (maximum glucose between 7.8 and 11.1 mmol L(-1)), 'unlikely diabetes' (maximum glucose between 6.0 and 7.8 mmol L(-1)), 'no diabetes' (maximum glucose < 6.0 mmol L(-1)) and 'unknown diabetes status' (no glucose test). We compared this electronic classification to a reference standard chart review performed by a blinded abstractor. RESULTS: A total of 500 hospitalizations were included. The prevalence of each diabetes group was: recognized - 17%; probable - 4%; possible - 15%; unlikely - 20%; none - 15%; and unknown - 29%. Our electronic algorithm correctly classified 88.8% (95% confidence interval 85.7-91.3) of hospitalizations (weighted-Kappa = 0.885; 95% confidence interval 0.851-0.919). The sensitivity, specificity and positive predictive values of our algorithm for 'known diabetes' was 0.842, 0.988 and 0.941, respectively. For patients at a 'high risk for diabetes' (maximum glucose > .8 mmol L(-1)), the corresponding values were 0.921, 0.971 and 0.872. CONCLUSIONS: Patients with diagnosed and undiagnosed diabetes can be accurately identified using electronic health care data.
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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 | 0.019 | 0.138 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".