Optimising fatigue, abdominal pain and faecal incontinence in people with inflammatory bowel disease (IBD-BOOST Optimise): feasibility study of a checklist and algorithm for initial nurse-led management
Bibliographic record
Abstract
OBJECTIVE: Many people with inflammatory bowel disease (IBD) experience fatigue, pain and faecal incontinence that some feel are inadequately addressed. It is unknown how many have potentially reversible medical issues underlying these symptoms. METHODS: We conducted a study testing the feasibility of a patient-reported symptom checklist and nurse-administered management algorithm ('Optimise') to manage common medical causes of IBD-related fatigue, pain and faecal incontinence. We conducted qualitative interviews with nurses implementing the algorithm. RESULTS: 515 individuals reporting IBD-related symptoms were invited to participate, of whom 201 (39%) consented. 194/201 (97%) returned the symptom checklist, of whom 157 (81%) returned a postal faecal calprotectin sample. Five (3%) participants reported 'red flags' and 31/157 (20%) participants had a faecal calprotectin result ≥200 µg/g, of whom 12 (8%) were judged to have likely active inflammation when clinical symptoms and disease history were reviewed. The algorithm suggested at least one clinical test or intervention for fatigue, pain or faecal incontinence in 67 (43%) participants, of whom 25 (37%) declined. Among 87 participants for whom clinical actions were indicated, 57 (66%) completed follow-up outcomes 3 months after algorithm implementation. Three nurses interviewed found the Optimise algorithm easy to administer. CONCLUSION: Implementing the Optimise checklist and algorithm appears feasible in UK clinical practice, with adjustments needed to minimise missing items. Not all patients accepted algorithm-indicated interventions, but a yield of 43% with symptoms having potentially reversible causes detected is clinically useful. Nurses endorsed ease and utility of the implementation process. Optimise now needs clinical effectiveness to be assessed.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".