Forecasting the Future: A Trek through the Changing Landscape of Inflammatory Bowel Disease
Bibliographic record
Abstract
Inflammatory bowel disease (IBD) is an immune-mediated disease of the gastrointestinal tract. It imparts a lifelong burden once diagnosed, which can lead to medication reliance, hospitalizations, and surgeries. Previous research has elucidated the current state of knowledge on IBD, but what is missing from the field are analyses of IBD-related outcomes within a specific population, and analyses of what these outcomes mean for the future of IBD in that population.1,2 Therefore, the aim of this thesis is to give an overarching understanding of the current burden of IBD; forecast the future burden; and, illustrate what these findings mean for the future of Canadians and our healthcare systems. Administrative data were used to identify prevalent cases from seven provinces (95% of the Canadian population). In Alberta specifically, prevalent and incident cases were isolated and data on hospitalizations, surgeries, medications, and all-cause mortality data were obtained. Using regression analyses, temporal trends of prevalence, incidence, hospitalization (total, IBD-related, and IBD-specific), surgery, biologics (an expensive medication increasing in popularity for the treatment of IBD), and mortality were analyzed. Data on prevalence from all seven provinces were analyzed and forecasted to 2030. Alberta-specific data were used to forecast incidence to 2030, and hospitalizations and surgeries to 2021. Overall, the prevalence of IBD in Canada is significantly increasing. By 2030, an estimated 402,853 Canadians will be living with IBD. In Alberta, incidence is forecasted to continue to significantly decrease from 2015 through to 2030. Hospitalizations and surgeries have also been significantly decreasing in Alberta and are forecasted to continue decreasing through to 2021. The proportion of patients dispensed biologics has been significantly increasing, which is indicative of an increasing utilization of this medication. Finally, the mortality rate has remained stable. While the decrease of adverse IBD-related outcomes (e.g., hospitalization and surgery) prove to be beneficial for patients with IBD and healthcare systems, the significant increase in the number of people with the disease may still overwhelm the system and inhibit patients from receiving necessary care.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".