Validation of administrative and clinical case definitions for gestational diabetes mellitus against laboratory results
Bibliographic record
Abstract
AIM: To examine the validity of International Classification of Disease, version 10 (ICD-10) codes for gestational diabetes mellitus in administrative databases (outpatient and inpatient), and in a clinical perinatal database (Alberta Perinatal Health Program), using laboratory data as the 'gold standard'. METHODS: Women aged 12-54 years with in-hospital, singleton deliveries between 1 October 2008 and 31 March 2010 in Alberta, Canada were included in the study. A gestational diabetes diagnosis was defined in the laboratory data as ≥2 abnormal values on a 75-g oral glucose tolerance test or a 50-g glucose screen ≥10.3 mmol/l. RESULTS: Of 58 338 pregnancies, 2085 (3.6%) met gestational diabetes criteria based on laboratory data. The gestational diabetes rates in outpatient only, inpatient only, outpatient or inpatient combined, and Alberta Perinatal Health Program databases were 5.2% (3051), 4.8% (2791), 5.8% (3367) and 4.8% (2825), respectively. Although the outpatient or inpatient combined data achieved the highest sensitivity (92%) and specificity (97%), it was associated with a positive predictive value of only 57%. The majority of the false-positives (78%), however, had one abnormal value on oral glucose tolerance test, corresponding to a diagnosis of impaired glucose tolerance in pregnancy. CONCLUSIONS: The ICD-10 codes for gestational diabetes in administrative databases, especially when outpatient and inpatient databases are combined, can be used to reliably estimate the burden of the disease at the population level. Because impaired glucose tolerance in pregnancy and gestational diabetes may be managed similarly in clinical practice, impaired glucose tolerance in pregnancy is often coded as gestational diabetes.
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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 | 0.046 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 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".