Validating Recipients of Pediatric Solid Organ Transplant Using Administrative Healthcare Data
Bibliographic record
Abstract
BACKGROUND: Health administrative datasets have the potential to provide valuable insights into pediatric solid organ transplantation; however, validation is necessary to ensure their accuracy. This study aimed to assess the validity of administrative data by comparing it to direct transplant records from a major pediatric transplant center in Ontario, Canada (1991-2011). METHODS: Using linked administrative healthcare databases, we conducted a retrospective analysis to evaluate the validity of physician billing claims and hospital diagnostic and procedural codes in identifying pediatric solid organ transplants. Sensitivity and positive predictive value (PPV) were calculated for various algorithms. RESULTS: During the study period, a total of 347 kidney, 250 liver, 200 heart, and 28 lung transplants were performed. The best algorithm for identifying these transplants utilized hospital procedural codes from the Canadian Institute for Health Information Discharge Abstract Database. Compared to transplant center records, these codes demonstrated a sensitivity of 91% (95% CI: 89-93) and PPV of 93% (95% CI: 91-95) when including all organ types, and performed similarly well when evaluating individual organ types. CONCLUSION: This study is the first to validate administrative data for identifying pediatric solid organ transplant recipients, demonstrating the reliability of procedural codes for population-level health research in this domain.
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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 | 0.014 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".