Unmet Medical Needs in Ulcerative Colitis: An Expert Group Consensus
Bibliographic record
Abstract
BACKGROUND: The authors aimed to conduct an extensive literature review and consensus meeting to identify unmet needs in ulcerative colitis (UC) and ways to overcome them. UC is a relapsing and remitting inflammatory bowel disease with varied, and changing, incidence rates worldwide. UC has an unpredictable disease course and is associated with a high health economic burden. During 2016 and 2017, a panel of experts was convened to identify, discuss and address areas of unmet need in UC. METHODS: PubMed and Cochrane Library databases were searched for relevant articles describing studies performed in patients with UC. These findings were used to generate a set of statements relating to unmet needs in UC. Consensus on these statements was then sought from a panel of 9 expert gastroenterologists using a modified Delphi review process that consisted of anonymous surveys followed by live meetings. RESULTS: In 2 literature reviews, over 5,000 unique records were identified and a total of 138 articles were fully reviewed. These were used to consider 26 areas of unmet need, which were explored in 2 face-to-face meetings, in which the statements were debated and amended, resulting in consensus on 30 final statements. The unmet needs identified were categorised into 7 areas: impact of UC on patients' daily life; importance of early diagnosis and treatment; drawbacks of existing treatments; urgent need for new treatments; and disease-, practice- or patient-focused unmet needs. CONCLUSIONS: These expert group meetings found a number of areas of unmet needs in UC, which is an important first step in tackling them in the future. Future research and development should be focused in these areas for the management of patients with UC.
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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 | 0.199 | 0.239 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.016 | 0.007 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".