Nutritional deficiencies and associated pathologies present unique challenges of diagnosing paediatric inflammatory bowel disease in sub-Saharan Africa
Bibliographic record
Abstract
Inflammatory bowel disease (IBD) represents two idiopathic conditions (Crohn’s and ulcerative colitis (UC)) of chronic inflammation of the gastrointestinal tract affecting all ages with no known cure. Approximately 4.9 million cases of IBD were reported in 2019, and this number is projected to increase. 1 IBD is driven by intestinal epithelial damage and dysfunction, dysbiotic gut microbiome and inappropriate immune response in genetically susceptible individuals. Clinical presentations are abdominal pain, diarrhoea, rectal bleeding, malnutrition and poor growth in children and adolescents.2 Diagnosis involves taking a comprehensive history, conducting relevant investigations, performing endoscopy and biopsy on patients with relevant clinical features by a well-trained clinician. Specific tissue histological findings such as neutrophilic inflammation, cryptitis and crypt abscess for UC and non-caseating granuloma in Crohn’s disease (CD) and radiological imaging are used to confirm the diagnosis.3 A delay in diagnosis or misdiagnosis resulting in untreated IBD can lead to worsened clinical outcomes. This includes persistent inflammation, development of systemic symptoms, potential complications like stenosis and fistulas with increased risk of bowel surgery.4 5 Compounding this problem are nutritional deficiencies, a frequent complication and comorbidity of IBD. Overt malnutrition (ie, undernutrition and overnutrition) like IBD is characterised by chronic inflammation, mucosal damage, intestinal barrier dysfunction, gut microbiome dysbiosis, altered immune response and malabsorption.6 In countries where childhood undernutrition is endemic, this presents an additional challenge in distinguishing malnutrition secondary to IBD. Additionally, nutritional deficiencies can worsen IBD pathophysiology and impede therapeutic response complicating disease management.6 This may lead to further misdiagnosis and underreporting of IBD’s prevalence in sub-Saharan Africa. Therefore, there is a crucial need to understand these challenges and underlying malnutrition in these settings. Moreover, reconciling malnutrition-driven pathologies may reveal unique subtypes of IBD, requiring a different approach to diagnosis and management.
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How this classification was reachedexpand
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 | 0.002 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".