Innovative Care for Inflammatory Bowel Disease Patients during the COVID-19 Pandemic: Use of Bedside Intestinal Ultrasound to Optimize Management
Bibliographic record
Abstract
Background: The COVID-19 pandemic caused by SARS-CoV-2 has reduced access to endoscopy and imaging. Safe alternatives, available at the bedside, are needed for accurate, non-invasive strategies to evaluate disease activity. The aim of this study is to establish the impact of clinic-based bedside intestinal ultrasound (IUS) on decision making, reduction in reliance on endoscopy and short-term healthcare utilization. Methods: We conducted a prospective observational evaluation during the COVID-19 pandemic, of the impact of a regional comprehensive care pathway to manage IBD patients consecutively recruited with acute symptoms, or suspected new diagnosis of IBD. Clinic-based access to sigmoidoscopy and bedside intestinal ultrasound were evaluated, used to direct clinical care and avoid hospitalization or hospital-based endoscopy. Results: A total of 72 patients were seen between March 15 and June 30, 2020. Of these, 57% (41/72) were female, 64% had Crohn's disease (46/72) with 14% (10/72) presenting with symptoms requiring investigation, of which 5 new cases of IBD were identified (50%). Immediate access to ultrasound and sigmoidoscopy led to meaningful changes in management in 80.5% (58/72) of patients. Active inflammation was detected by IUS alone (72.5%, 29/40) or in combination with in-clinic sigmoidoscopy (78%, 18/23) or sigmoidoscopy alone (78% 7/9). Six patients were referred to colorectal surgery for urgent surgical intervention including two patients admitted directly. Conclusion: Implementation of IUS as part of a clinical care pathway during the COVID-19 pandemic is a useful strategy to enhance care delivery and improve clinical decisions, while sparing other important acute care resources.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".