The use of ICD codes to identify IBD subtypes and phenotypes of the Montreal classification in the Swedish National Patient Register
Bibliographic record
Abstract
Introduction: Whether data on International Classification of Diseases (ICD)-codes from the Swedish National Patient Register (NPR) correctly correspond to subtypes of inflammatory bowel disease (IBD) and phenotypes of the Montreal classification scheme among patients with prevalent disease is unknown. Materials and methods: We obtained information on IBD subtypes and phenotypes from the medical records of 1403 patients with known IBD who underwent biological treatment at ten Swedish hospitals and retrieved information on their IBD-associated diagnostic codes from the NPR. We used previously described algorithms to define IBD subtypes and phenotypes. Finally, we compared these register-generated subtypes and phenotypes with the corresponding information from the medical records and calculated positive predictive values (PPV) with 95% confidence intervals. Results: Among patients with clinically confirmed disease and diagnostic listings of IBD in the NPR (N = 1401), the PPV was 97 (96–99)% for Crohn’s disease, 98 (97–100)% for ulcerative colitis, and 8 (4–11)% for IBD-unclassified. The overall accuracy for age at diagnosis was 95% (when defined as A1, A2, or A3). Examining the validity of codes representing disease phenotype, the PPV was 36 (32–40)% for colonic Crohn’s disease (L2), 61 (56–65)% for non-stricturing/non-penetrating Crohn’s disease behaviour (B1) and 83 (78–87)% for perianal disease. Correspondingly, the PPV was 80 (71–89)% for proctitis (E1)/left-sided colitis (E2) in ulcerative colitis. Conclusions: Among people with known IBD, the NPR is a reliable source of data to classify most subtypes of prevalent IBD, even though misclassification commonly occurred in Crohn’s disease location and behaviour and also among IBD-unclassified patients.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".