Systematic review with meta‐analysis: high prevalence and cost of continued aminosalicylate use in patients with ulcerative colitis escalated to immunosuppressive and biological therapies
Bibliographic record
Abstract
BACKGROUND: Aminosalicylates are the most frequently prescribed treatment for ulcerative colitis (UC). In the absence of empirical evidence, clinicians are uncertain whether to continue aminosalicylates in patients with UC after escalating therapy. AIMS: To quantify concomitant aminosalicylate use in UC randomised clinical trials (RCTs), identify factors associated with their use, and estimate treatment costs of concomitant aminosalicylate therapy. METHODS: MEDLINE, Embase, and CENTRAL were searched from inception to 1 March 2017 for placebo-controlled RCTs of immunosuppressants, biologics, or oral small molecules in adults with UC. The proportion of patients prescribed concomitant aminosalicylates at trial entry was pooled using a random-effects model. Meta-regression was performed to assess trial-level factors associated with aminosalicylate use. Treatment costs were estimated using 2018 formulary data from five Canadian provinces. RESULTS: = 95%). In univariable meta-regression, aminosalicylate use was not associated with trial design, setting, year of publication, disease severity, disease duration, or drug class. The estimated direct annual treatment cost of concomitant aminosalicylates is ~$20 million for the Canadian UC population, assuming conservative estimates of UC prevalence, aminosalicylate use and dose, and the lowest cost formulation. CONCLUSIONS: Approximately 80% of UC patients entering clinical trials of immunosuppressants, biologics, or oral small molecules continue to use aminosalicylates. An RCT is needed to inform the benefits and harms of continuing vs stopping aminosalicylates in patients escalating therapy.
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 | 0.018 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.049 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".