Validation of administrative data case definitions for gestational diabetes mellitus
Bibliographic record
Abstract
AIM: To examine, using administrative data, the validity of two algorithms for identifying gestational diabetes mellitus: 1) the current National Diabetes Surveillance System algorithm for excluding gestational diabetes cases and 2) gestational diabetes-specific ICD codes in the delivery-related hospitalization. METHODS: This was a retrospective study of all women, aged 18-54 years, residing in Alberta, Canada, with singleton deliveries between 1 April 1999 and 31 March 2010. We linked Alberta Perinatal Health Program data on all deliveries to administrative claims data from Alberta Health using the mother's personal health number. For both gestational diabetes algorithms, we calculated the sensitivity, specificity, positive predictive value, negative predictive value and agreement, using gestational diabetes identified in the Alberta Perinatal Health Program as the 'gold standard'. RESULTS: Our study sample consisted of 411 390 deliveries for 273 152 women. The mean (sd) age was 29.1 (5.6) years and 82.3% of the women were white. Crude rates of gestational diabetes were 3.9% (16 215 cases), 1.3% (5189 cases) and 4.0% (16 440 cases) according to the Alberta Perinatal Health Program, National Diabetes Surveillance System and ICD code-based algorithms, respectively. Compared with the Alberta Perinatal Health Program database, the National Diabetes Surveillance System algorithm had a sensitivity of 25% and specificity of 100%, whereas the gestational diabetes-specific ICD code-based algorithm had a sensitivity of 86% and specificity of 99%. CONCLUSIONS: The National Diabetes Surveillance System algorithm underestimates the number of gestational diabetes cases. A more valid mechanism to identify gestational diabetes prevalence using health administrative data is the use of gestational diabetes-specific ICD-9/10 codes in the delivery hospitalization.
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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 | 0.050 | 0.195 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| 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".