Modelling the benefits of an optimised treatment strategy for 5-ASA in mild-to-moderate ulcerative colitis
Bibliographic record
Abstract
OBJECTIVES: 5-aminosalicylate (mesalazine; 5-ASA) is an established first-line treatment for mild-to-moderate ulcerative colitis (UC). This study aimed to model the benefits of optimising 5-ASA therapy. METHODS: A decision tree model followed 10 000 newly diagnosed patients with mild-to-moderately active UC through induction and 1 year of maintenance treatment. Optimised treatment (maximising dose of 5-ASA and use of combined oral and rectal therapy before treatment escalation) was compared with standard treatment (standard doses of 5-ASA without optimisation). Modelled data were derived from published meta-analyses. The primary outcomes were patient numbers achieving and maintaining remission, with an analysis of treatment costs for each strategy conducted as a secondary outcome (using UK reference costs). RESULTS: During induction, there was a 39% increase in patients achieving remission through the optimised pathway without requiring systemic steroids and/or biologics (6565 vs 4725 for standard). Potential steroidal/biological adverse events avoided included: seven venous thromboembolisms and eight serious infections. Out of the 6565 patients entering maintenance following successful induction on 5-ASA, there was a 21% reduction in relapses when optimised (1830 vs 2311 for standard). This translated into 297 patients avoiding further systemic steroids and 214 biologics. Optimisation led to an average net saving of £272 per patient entering the model for the induction and maintenance of remission over 1 year. CONCLUSION: Modelling suggests that optimising 5-ASA therapy (both the inclusion of rectal 5-ASA into a combined oral and rectal regimen and maximisation of 5-ASA dose) has clinical and cost benefits that supports wider adoption in clinical practice.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".