Determination of the optimal case definition for the diagnosis of end-stage renal disease from administrative claims data in Manitoba, Canada
Bibliographic record
Abstract
INTRODUCTION: End-stage renal disease (ESRD) is a major public health problem with increasing prevalence and costs. An understanding of the long-term trends in dialysis rates and outcomes can help inform health policy. We determined the optimal case definition for the diagnosis of ESRD using administrative claims data in the province of Manitoba over a 7-year period. METHODS: We determined the sensitivity, specificity, predictive value and overall accuracy of 4 administrative case definitions for the diagnosis of ESRD requiring chronic dialysis over different time horizons from Jan. 1, 2004, to Mar. 31, 2011. The Manitoba Renal Program Database served as the gold standard for confirming dialysis status. RESULTS: During the study period, 2562 patients were registered as recipients of chronic dialysis in the Manitoba Renal Program Database. Over a 1-year period (2010), the optimal case definition was any 2 claims for outpatient dialysis, and it was 74.6% sensitive (95% confidence interval [CI] 72.3%-76.9%) and 94.4% specific (95% CI 93.6%-95.2%) for the diagnosis of ESRD. In contrast, a case definition of at least 2 claims for dialysis treatment more than 90 days apart was 64.8% sensitive (95% CI 62.2%-67.3%) and 97.1% specific (95% CI 96.5%-97.7%). Extending the period to 5 years greatly improved sensitivity for all case definitions, with minimal change to specificity; for example, for the optimal case definition of any 2 claims for dialysis treatment, sensitivity increased to 86.0% (95% CI 84.7%-87.4%) at 5 years. CONCLUSION: Accurate case definitions for the diagnosis of ESRD requiring dialysis can be derived from administrative claims data. The optimal definition required any 2 claims for outpatient dialysis. Extending the claims period to 5 years greatly improved sensitivity with minimal effects on specificity for all case definitions.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".