Multinational evaluation of clinical decision-making in the treatment and management of mild-to-moderate ulcerative colitis
Bibliographic record
Abstract
OBJECTIVES: To understand current thinking and clinical decision-making in the treatment and management of patients with mild-to-moderate ulcerative colitis (UC). METHODS: This multinational, survey-based study was conducted in 2021. Two meetings were held, involving 11 IBD specialists, that used a series of questions and discussion to identify all factors possibly related to the management of UC. The importance of identified factors was assessed using an online questionnaire covering three scenarios - active disease, remission and patient empowerment. Each factor was scored on a scale of 0 (very-unimportant) to 100 (very-important) within each scenario, by a separate group of healthcare professionals working in IBD. RESULTS: A total of 157 individual factors were identified by the 11 IBD specialists and scored in the three scenarios by 56 respondents (52; 93% specialist gastroenterologists) from Europe and North America (25; 45%), South America (19; 34%) and the Middle East, Asia and Australia (12; 21%). For all scenarios, factors related to educating patients regarding UC and its treatment and understanding of patient goals ranked highest, ahead of clinical considerations regarding disease activity and treatment history. Setting realistic short-term treatment targets was a key consideration. 5-ASA optimisation and use of faecal calprotectin monitoring were core strategies across the three scenarios tested. Support for patients during longer-term management of their disease, starting from initial flare, was an important recurring theme. CONCLUSION: The current management approach for mild-to-moderate UC was found to be guided primarily by the patient's perspectives and goals, alongside assessment of their medical and disease history.
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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 | 0.041 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".