Estimates of Disease Course in Inflammatory Bowel Disease Using Administrative Data: A Population-level Study
Bibliographic record
Abstract
BACKGROUND AND AIMS: We sought develop a predictive model of disease course in inflammatory bowel disease [IBD] using health care utilization measures from administrative health data, and to apply this model to estimate disease course at a population level over time. METHODS: Study participants were IBD patients who were prospectively followed for a 1-year period between 2009 and 2010 in a Canadian clinic setting to assess their IBD disease course [i.e. remission, mild, moderate, severe]. Clinic data were linked with population-based administrative health data. A multivariable partial proportional odds model tested health care utilization measures that discriminated disease course groups. The model was applied to project the distribution of disease course for the Manitoba IBD population for 1995-2013. RESULTS: There were 407 participants (54.3% females, 64.4% Crohn's disease [CD]) with mean age at diagnosis of 29.8 years [SD 14.9]. Forty-one per cent of participants were clinically in remission, while 14.0% had severe IBD. Mild, moderate or severe disease was associated with three or more gastroenterologist visits (odds ratio [OR] = 3.33, 95% confidence interval [CI]: 2.03-5.54) or three or more general practitioner visits [OR = 2.97, 95% CI: 1.44-6.37] with an IBD diagnosis and ≥1 radiology test [OR = 2.22, 95% CI: 1.31-3.80]. The percentages of the Manitoba IBD population in remission rose steadily from 1995 to 2013 [43.6 to 59.9%], while the percentages of individuals with mild, moderate or severe disease declined. CONCLUSION: This study demonstrated that health care utilization measures from administrative data can be used to predict disease course in the IBD population.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".