The Burden of Biopsy-Proven Pediatric Celiac Disease in Ontario, Canada: Derivation of Health Administrative Data Algorithms and Determination of Health Services Utilization
Bibliographic record
Abstract
Introduction: The main objective of this thesis is to develop an algorithm to accurately identify cases of biopsy-proven Celiac Disease (CD) in children aged 6 months-14 years old from Ontario health administrative data. Method: CD cases diagnosed in 2005-2011 were identified from CHEO, and linked to the health administrative data to serve as reference for algorithms derivation. Algorithms based on outpatient physician visits for CD plus endoscopy billing code were constructed and tested. Results: The best algorithm selected based on performance from derivation study and clinical expertise consisted of an OHIP-based endoscopy billing claim followed by 1 or more adult or pediatric gastroenterologist encounters after the endoscopic procedure. The sensitivity, specificity, PPV, and NPV for the algorithm were 70.4%, >99.9%, 53.3% and >99.9% respectively. Conclusion: Study results suggest that the currently available Ontario health administrative data is not suitable for identifying incident pediatric CD cases.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".