The 2023 Impact of Inflammatory Bowel Disease in Canada: Treatment Landscape
Bibliographic record
Abstract
The therapeutic landscape for inflammatory bowel disease (IBD) has changed considerably over the past two decades, owing to the development and widespread penetration of targeted therapies, including biologics and small molecules. While some conventional treatments continue to have an important role in the management of IBD, treatment of IBD is increasingly moving towards targeted therapies given their greater efficacy and safety in comparison to conventional agents. Early introduction of these therapies-particularly in persons with Crohn's disease-combining targeted therapies with traditional anti-metabolite immunomodulators and targeting objective markers of disease activity (in addition to symptoms), have been shown to improve health outcomes and will be increasingly adopted over time. The substantially increased costs associated with targeted therapies has led to a ballooning of healthcare expenditure to treat IBD over the past 15 years. The introduction of less expensive biosimilar anti-tumour necrosis factor therapies may bend this cost curve downwards, potentially allowing for more widespread access to these medications. Newer therapies targeting different inflammatory pathways and complementary and alternative therapies (including novel diets) will continue to shape the IBD treatment landscape. More precise use of a growing number of targeted therapies in the right individuals at the right time will help minimize the development of expensive and disabling complications, which has the potential to further reduce costs and improve outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".