Review article: the diagnosis and management of Crohn’s disease in populations with high‐risk rates for tuberculosis
Bibliographic record
Abstract
BACKGROUND: Distinguishing Crohn's disease from intestinal tuberculosis in endemic areas is challenging as both conditions have overlapping clinical, radiological, endoscopic and histological characteristics. Furthermore, high rates of latent tuberculosis confer a considerable risk of reactivation once therapy for established Crohn's disease is started. AIM: To review current strategies in differentiating these two conditions, and in managing Crohn's disease, in populations with high rates of tuberculosis. METHODS: Literature review and clinical experience. RESULTS: While various clinical, radiological, endoscopic and histological parameters may aid in differentiating Crohn's disease from intestinal tuberculosis, these remain imperfect and as treatment options differ misdiagnosis has grave consequences. We propose a diagnostic algorithm, based on currently available evidence and experience, to aid in this dilemma. We also discuss approaches to the management of Crohn's disease, including agents targeting tumour necrosis factor-alpha, in patients at risk of developing tuberculosis. CONCLUSIONS: A diagnosis of Crohn's disease in individuals at risk for tuberculosis should only be made after careful interpretation of clinical signs, abdominal imaging and systematic endoscopic and histological assessment. Newer techniques for the diagnosis of latent tuberculosis still need to be validated in this environment, and guidelines on the treatment of latent tuberculosis in this setting require clarification.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".