Optimising the Inflammatory Bowel Disease Unit to Improve Quality of Care: Expert Recommendations: Table 1.
Bibliographic record
Abstract
INTRODUCTION: The best care setting for patients with inflammatory bowel disease [IBD] may be in a dedicated unit. Whereas not all gastroenterology units have the same resources to develop dedicated IBD facilities and services, there are steps that can be taken by any unit to optimise patients' access to interdisciplinary expert care. A series of pragmatic recommendations relating to IBD unit optimisation have been developed through discussion among a large panel of international experts. METHODS: Suggested recommendations were extracted through systematic search of published evidence and structured requests for expert opinion. Physicians [n = 238] identified as IBD specialists by publications or clinical focus on IBD were invited for discussion and recommendation modification [Barcelona, Spain; 2014]. Final recommendations were voted on by the group. Participants also completed an online survey to evaluate their own experience related to IBD units. RESULTS: A total of 60% of attendees completed the survey, with 15% self-classifying their centre as a dedicated IBD unit. Only half of respondents indicated that they had a defined IBD treatment algorithm in place. Key recommendations included the need to develop a multidisciplinary team covering specifically-defined specialist expertise in IBD, to instil processes that facilitate cross-functional communication and to invest in shared care models of IBD management. CONCLUSIONS: Optimising the setup of IBD units will require progressive leadership and willingness to challenge the status quo in order to provide better quality of care for our patients. IBD units are an important step towards harmonising care for IBD across Europe and for establishing standards for disease management programmes.
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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 | 0.024 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".