Identifying patients with diagnosed cirrhosis in administrative health databases: a validation study
Bibliographic record
Abstract
Objectives: Case ascertainment algorithms were developed and validated to identify people living with cirrhosis in administrative health data in Manitoba, Canada using primary care electronic medical records (EMR) to define the reference standards. Methods: We linked provincial administrative health data to primary care EMR data. The validation cohort included 116,675 Manitobans aged >18 years with at least one primary care visit between April 1998 and March 2015. Hospital records, physician billing claims, vital statistics, and prescription drug data were used to develop and test 93 case-finding algorithms. A validated case definition for primary care EMR data was the reference standard. We estimated sensitivity, specificity, positive and negative predictive values (PPV, NPV), Youden's index, area under the receiver operative curve, and their 95% confidence intervals (CIs). Results: = 1593). Algorithm sensitivity estimates ranged from 32.5% (95% CI 32.2-32.8) to 68.3% (95% CI 68.0-68.9) and PPV from 17.4% (95% CI 17.1-17.6) to 23.4% (95% CI 23.1-23.6). Specificity (95.5-98.2) and NPV (approximately 99%) were high for all algorithms. The algorithms had slightly higher sensitivity estimates among men compared with women, and individuals aged ≥45 years compared to those aged 18-44 years. Conclusion: Cirrhosis algorithms applied to administrative health data had moderate validity when a validated case definition for primary care EMRs was the reference standard. This study provides algorithms for identifying diagnosed cirrhosis cases for population-based research and surveillance studies.
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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 | 0.032 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".