Unmet Medical Needs in Ulcerative Colitis: An Expert Group Consensus
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it