Validating a case definition for chronic kidney disease using administrative data
Bibliographic record
Abstract
BACKGROUND: Administrative data are commonly used for surveillance of chronic medical conditions. The purpose of this study was to determine the validity of an algorithm derived from administrative data for identifying chronic kidney disease (CKD) compared to the reference standard of estimated glomerular filtration rate (eGFR). METHODS: We identified adults from the province of Alberta with at least two outpatient serum creatinine measurements within a 1-year time period. Validity indices were estimated for CKD using up to 3 years of administrative data (physician billing claims and hospital discharge abstracts) for various case-definition combinations. For each algorithm, the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated against two reference standard definitions of CKD (two eGFR measurements <60 mL/min/1.73m(2) or mean eGFR < 30 mL/min/1.73m(2)). RESULTS: A total of 321 293 eligible subjects were identified. Irrespective of the algorithm, sensitivities for defining CKD (eGFR < 60 mL/min/1.73m(2)) using administrative codes were low. A case-definition algorithm employing two physician claims or one hospitalization within a 2-year period had sensitivity of 19.4%, specificity of 97.2%, PPV of 60.1% and NPV of 84.8% for detecting CKD. Estimates of sensitivity were higher when <30 mL/min/1.73m(2) was used as the reference standard, although PPVs were lower and consistently less than 50%. CONCLUSION: These results, using eGFR as a reference standard, suggest that administrative data have insufficient sensitivity and PPV for CKD surveillance, although they may be useful when highly specific algorithms are required for research purposes.
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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 | 0.062 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| 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".