Systematic review of validated case definitions for diabetes in ICD-9-coded and ICD-10-coded data in adult populations
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVES: With steady increases in 'big data' and data analytics over the past two decades, administrative health databases have become more accessible and are now used regularly for diabetes surveillance. The objective of this study is to systematically review validated International Classification of Diseases (ICD)-based case definitions for diabetes in the adult population. SETTING, PARTICIPANTS AND OUTCOME MEASURES: Electronic databases, MEDLINE and Embase, were searched for validation studies where an administrative case definition (using ICD codes) for diabetes in adults was validated against a reference and statistical measures of the performance reported. RESULTS: The search yielded 2895 abstracts, and of the 193 potentially relevant studies, 16 met criteria. Diabetes definition for adults varied by data source, including physician claims (sensitivity ranged from 26.9% to 97%, specificity ranged from 94.3% to 99.4%, positive predictive value (PPV) ranged from 71.4% to 96.2%, negative predictive value (NPV) ranged from 95% to 99.6% and κ ranged from 0.8 to 0.9), hospital discharge data (sensitivity ranged from 59.1% to 92.6%, specificity ranged from 95.5% to 99%, PPV ranged from 62.5% to 96%, NPV ranged from 90.8% to 99% and κ ranged from 0.6 to 0.9) and a combination of both (sensitivity ranged from 57% to 95.6%, specificity ranged from 88% to 98.5%, PPV ranged from 54% to 80%, NPV ranged from 98% to 99.6% and κ ranged from 0.7 to 0.8). CONCLUSIONS: Overall, administrative health databases are useful for undertaking diabetes surveillance, but an awareness of the variation in performance being affected by case definition is essential. The performance characteristics of these case definitions depend on the variations in the definition of primary diagnosis in ICD-coded discharge data and/or the methodology adopted by the healthcare facility to extract information from patient records.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it