Administrative Databases: Friend or Foe in Paediatric Cardiomyopathy
Bibliographic record
Abstract
Background: Cardiomyopathy (CM) is a rare childhood disease associated with morbidity and mortality. Limited data exist on paediatric CM in Canada. Given the rare nature, single-centre studies are not sufficiently powered to address important questions. Therefore, administrative health data may serve as a resource for the study of childhood CM. The goal of this study was to validate the accuracy of International Classification of Diseases (ICD)-based algorithms to identify paediatric CM in health databases using a clinical registry as the gold standard. Methods: The clinical registry was compiled from outpatient and inpatient records at the Stollery Children's Hospital (January 1, 2013, to December 31, 2021). Patients were categorized as having CM or screened without CM. Data were linked to administrative health databases using the patient's Unique Lifetime Identifier. Algorithms based on the presence of ICD, 10th Revision, codes for CM were then evaluated, and cross-tabulations against the clinical registry were generated. Accuracy, positive predictive value, negative predictive value, sensitivity, and specificity were calculated. Results: The clinical registry had 90 patients with CM and 249 screened without CM. The algorithms ruled out CM (high negative predictive value) but had variability in the ability to diagnose CM positive predictive value. The algorithm that performed the best was based on a diagnosis of CM in a hospitalization or 2 ambulatory visits. Conclusions: A combination of inpatient and outpatient databases can be used, with acceptable accuracy, to identify paediatric patients with CM. This finding allows for the use of the identified algorithm for the comprehensive study of paediatric CM in Canada.
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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 | 0.022 | 0.152 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".