Validation of a case definition to define chronic dialysis using outpatient administrative data
Bibliographic record
Abstract
BACKGROUND: Administrative health care databases offer an efficient and accessible, though as-yet unvalidated, approach to studying outcomes of patients with chronic kidney disease and end-stage renal disease (ESRD). The objective of this study is to determine the validity of outpatient physician billing derived algorithms for defining chronic dialysis compared to a reference standard ESRD registry. METHODS: A cohort of incident dialysis patients (Jan. 1-Dec. 31, 2008) and prevalent chronic dialysis patients (Jan 1, 2008) was selected from a geographically inclusive ESRD registry and administrative database. Four administrative data definitions were considered: at least 1 outpatient claim, at least 2 outpatient claims, at least 2 outpatient claims at least 90 days apart, and continuous outpatient claims at least 90 days apart with no gap in claims greater than 21 days. Measures of agreement of the four administrative data definitions were compared to a reference standard (ESRD registry). Basic patient characteristics are compared between all 5 patient groups. RESULTS: 1,118,097 individuals formed the overall population and 2,227 chronic dialysis patients were included in the ESRD registry. The three definitions requiring at least 2 outpatient claims resulted in kappa statistics between 0.60-0.80 indicating "substantial" agreement. "At least 1 outpatient claim" resulted in "excellent" agreement with a kappa statistic of 0.81. CONCLUSIONS: Of the four definitions, the simplest (at least 1 outpatient claim) performed comparatively to other definitions. The limitations of this work are the billing codes used are developed in Canada, however, other countries use similar billing practices and thus the codes could easily be mapped to other systems. Our reference standard ESRD registry may not capture all dialysis patients resulting in some misclassification. The registry is linked to on-going care so this is likely to be minimal. The definition utilized will vary with the research objective.
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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 | 0.157 | 0.275 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| 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".