Validation of ulcerative colitis and Crohn’s disease and their phenotypes in the Danish National Patient Registry using a population-based cohort
Bibliographic record
Abstract
The Danish National Patient Registry (DNPR) has been the source of several epidemiological studies of inflammatory bowel disease (IBD). However, the validation dates back to 1996 and lacks outpatient records and disease classification. The aim of this study was to update the validation and assess the validity and reliability of using the registry in disease classification. Validation of the registry was done using a population-based inception cohort of IBD patients from 2003 to 2011 consisting of 513 patients. Specificity and sensitivity were calculated for the diagnoses of Crohn’s disease (CD) and ulcerative colitis (UC), age at diagnosis and disease classification according to the Montreal Classification at both time of diagnosis and end of follow-up. The registry showed high validity and reliability in identifying CD and UC patients concerning correct age classification and identifying perianal disease. The registry showed inconsistent, unreliable results in further disease classification. The DNPR has good validity and reliability in identifying patients with CD and UC, and defining the age of patients at diagnosis. However, categorising IBD patients according to the Montreal Classification should not be carried out using DNPR data in their current form, except when identifying CD patients with perianal disease.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".