Non‐invasive monitoring and treat‐to‐target approach are cost‐effective in patients with mild–moderate ulcerative colitis
Bibliographic record
Abstract
BACKGROUND: There are no data to assess the value associated with a treat-to-target (T2T) strategy based on tight control of mild-moderate ulcerative colitis (UC). AIM: To assess the cost-effectiveness of a T2T approach based on the normalisation of clinical signs and faecal calprotectin (FC) METHODS: A decision analytical Markov model was developed to compare T2T algorithm combining clinical symptoms and FC levels to define treatment response and the possible switch to the next treatment line (T2T-FC), and the reference strategy based only on symptoms. The model included five treatment lines and was conducted from the Italian national health service (NHS) perspective using a 3-year time horizon. The model calculated the incremental cost-effectiveness ratio as € per relapse avoided. Deterministic and probabilistic sensitivity analyses were conducted. RESULTS: The cost-effectiveness analysis produced an increased time spent by a patient in clinical remission and FC ≤ 100 level (+0.177 years; about 2 months) and a decreasing number of relapses (-0.1937; -20.9%) per patient using a T2T-FC approach compared to only symptoms. Furthermore, the T2T-FC was associated with higher cost (+€1795). The ICER estimated was €9263 per relapse avoided. These results were confirmed by sensitivity analyses. CONCLUSIONS: T2T-FC approach resulted in a higher benefit for mild-moderate UC patients in terms of time in remission and incidence of relapse but was associated with higher costs. Clinical trials and real-world clinical studies are needed to provide additional data on the cost-benefit of this approach.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".