IBD2020 global forum: results of an international patient survey on quality of care
Bibliographic record
Abstract
BACKGROUND/AIMS: IBD2020 is a global forum for standards of care in inflammatory bowel disease (IBD). The aim of the IBD2020 survey was to identify and describe variations in quality care of IBD. METHODS: Patients with IBD from Finland, Italy, France, Canada, Germany, UK, Spain and Sweden were surveyed during 2013 to 2014, covering: disease characteristics; impact on life and work; organization and perceived quality of care. RESULTS: Seven thousand five hundred and seven patients participated (median age, 39 years [range, 10-103 years]; 2,354 male [31.4%]), including 4,097 (54.6%) with Crohn's disease (CD) and 3,410 (45.4%) with ulcerative colitis (UC). Median time from symptom onset to diagnosis was 1 year for both CD (range, 0-47 years) and UC (range, 0-46 years), with no clear evidence of improvement in diagnostic delay over the preceding 24 years. Half of the patients (3,429; 50.0%) rated their care as "excellent" or "very good," with similar results for CD and UC across countries. Five factors were significantly (P<0.01) associated with perceived good quality of care: quality of specialist communication; review consultation being long enough; failure to share information; no access to a dietician; speed of advice. CONCLUSIONS: The IBD2020 survey has highlighted areas related to quality of care of IBD from the patients' perspective, with scope for improvement.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".