Development of an administrative definition for celiac disease
Bibliographic record
Abstract
OBJECTIVE: The investigation and management of celiac disease places a high burden on the health care system. Accurate methods to ascertain cases of celiac disease (CD) in population-based administrative data can facilitate epidemiologic and health services research to guide disease management. The study aim was to develop and validate administrative data case definitions for CD to facilitate further studies about the effect of CD on osteoporosis and fracture risk. RESULTS: Population-based data from the Manitoba Bone Mineral Density (BMD) Program registry, which contains medical information on all individuals in the province of Manitoba, Canada who have received BMD testing, was used to define the study cohort. Linked hospital discharge abstracts and physician billing claims were used to ascertain diagnoses of celiac disease in administrative data. A population-based CD serologic registry was used as the validation database. One diagnosis code in hospital discharge abstracts or two or more diagnosis codes in physician billing claims optimized the detection of positive celiac serology with sensitivity of 84% (95% CI 80-88%), specificity of 97% (95% CI 80-88%), PPV of 80% (95% CI 80-88%), and NPV of 97% (95% CI 80-88%). Our administrative data case definition for celiac disease demonstrates good sensitivity and specificity for detecting positive celiac serology.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".