Comorbidity before and after a diagnosis of inflammatory bowel disease
Bibliographic record
Abstract
BACKGROUND: Comorbidity is an important predictor of how disease course in inflammatory bowel (IBD) evolves. AIMS: To determine pre-diagnosis relative rates (RR) and post-diagnosis hazard ratios (HR) of component diseases of the Charlson Comorbidity Index (CCI) in a cohort study of persons with IBD. METHODS: The University of Manitoba IBD Epidemiology Database includes all Manitobans with IBD from 1 April 1984 through 31 March 2018 and matched controls. All outpatient physician claims and hospital discharge abstracts were searched for diagnostic codes for CCI component diseases. Some diseases were collapsed into one group such that we assessed 12 conditions. We report the RR of these conditions prior to IBD and the incidence of these diagnoses after IBD. Using Cox proportional hazards regression we report post-diagnosis HR. Confidence intervals were adjusted for Bonferroni correction. RESULTS: The RR of cardiovascular diseases, peripheral vascular diseases, chronic pulmonary diseases, connective tissue disease/rheumatic diseases, renal disease, liver diseases, peptic ulcer disease, and cancer were all increased prior to diagnoses of IBD compared to controls. All comorbidities were increased post IBD diagnosis. The increased HR for dementia in persons with Crohn's disease was a concerning novel finding. The increased association with paraplegia/hemiplegia was unexpected. For all comorbidities, except diabetes, the age at diagnosis was younger in IBD than controls. CONCLUSIONS: Persons with IBD have a higher comorbidity burden than persons without IBD. Optimal care plans for persons with IBD should include an assessment for other comorbidities that include just about every other organ system.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".