Positioning Treatments for Ulcerative Colitis: A RAND Appropriateness Panel
Bibliographic record
Abstract
Introduction: Therapeutic options for the treatment of ulcerative colitis (UC) have increased recently. However, there is a relative lack of comparative effectiveness data to support decisions regarding choice of therapies in UC. We therefore applied the RAND Appropriateness Method toward establishing the appropriateness of treatment positioning for UC. Methods: A comprehensive literature review was performed regarding the use of current medical therapy in mild and moderate UC. This review was presented to an expert panel comprised of 14 IBD experts from around the world. 225 scenarios were rated for the appropriateness of various UC treatments on a 1-9 scale (1-3 inappropriate, 4-6 uncertain, 7-9 appropriate) using a modified Delphi method through a web-based survey. The group subsequently met to discuss and re-rate the scenarios. Disagreement was assessed using a validated index, and scenarios with disagreement were automatically rated “uncertain”. Results: There was agreement amongst the panel in 224/225 scenarios. The scenario in which there was disagreement related to the use of steroids in a patient with left sided or extensive colitis who was losing response to a second anti-TNF agent. Referral for surgery was rated “appropriate” in 4 scenarios, generally in the setting of failed biologic therapy, and “inappropriate” in 23 scenarios, mostly relating to mild or limited disease. The role of surgery in patients who still had treatment options available after failure of some treatments was often deemed “uncertain”. There was also uncertainty about the addition of thiopurines or steroids in the context of failing biologic therapy, and about the role of dose escalation of vedolizumab for primary non-response; escalation for secondary loss of response was generally deemed appropriate. The use of anti-TNF therapy or vedolizumab in steroid-dependent and thiopurine-resistant patients was generally deemed appropriate. Similarly, switching between vedolizumab and anti-TNF in the context of treatment failure was generally considered “appropriate”. Conclusion: Despite there being limited data to guide positioning of UC treatments, there was general agreement that both anti-TNF and vedolizumab were appropriate options in a wide variety of clinical scenarios. The introduction of new therapies has likely made referral for surgery less appropriate than previously and has made the appropriateness of steroids and thiopurines less clear in some scenarios.
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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 | 0.195 | 0.229 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".