Predictors of aetiology and outcomes of acute gastrointestinal illness in returning travellers: a retrospective cohort analysis
Bibliographic record
Abstract
BACKGROUND: Gastrointestinal illness is a major cause of morbidity in travellers and is a common reason for presentation to healthcare services on return. Whilst the aetiology of imported gastrointestinal disease is predominantly infectious, outcomes are variable due to a range of phenomena such as post-infectious irritable bowel syndrome, drug resistance and occult pathology (both infectious and non-infectious). Previous studies have focussed on predictors of aetiology of gastrointestinal disease in travellers; we present a retrospective study combining both aetiological and early outcome data in a large cohort of returned travellers. METHOD: We identified 1450 patients who attended our post-travel walk-in clinic with gastrointestinal symptoms between 2010 and 2016. Demographic, travel, clinical and laboratory data was collected through case note review. Logistic regression analysis to examine correlates of aetiology and outcome were performed in R (CRAN Project 2017). RESULTS: Of 1450 patients in our cohort 153 reported bloody diarrhoea and 1081 (74.6%) reported non-bloody diarrhoea. A definitive microbiological diagnosis was made in 310 (20.8%) of which 137 (9.4%) had a parasite identified and 111 (7.7%) had a bacterial cause identified. Factors associated with a parasitological diagnosis included history of travel to South Asia (aOR = 2.55; 95%CI 1.75-3.70, p < 0.0001) and absence of bloody diarrhoea (aOR = 0.22; 95%CI 0.066-0.53, p < 0.005). Factors associated with a bacteriological diagnosis included male gender (aOR = 1.69; 95%CI 1.10-2.62, p < 0.05), an age < 37 years on presentation (aOR = 2.04; 95%CI 1.25-3.43, p < 0.01), white cells on stool microscopy (aOR = 3.52; 95%CI 2.09-5.86, p < 0.0001) and a C-reactive protein level of >5iu/dL (aOR = 4.68; 95%CI 2.91-7.72, p < 0.0001). The majority (1235/1450, 82.6%) reported full symptomatic resolution by the first follow up visit; factors associated with lack of symptomatic resolution included female gender (aOR = 1.45 95%CI 1.06-1.99, p < 0.05), dysenteric diarrhoea (aOR = 2.14 (95%CI 1.38-3.25, p < 0.0005) and elevated peripheral leukocyte count (aOR = 1.58 95%CI 1.02-2.40, p < 0.05). CONCLUSIONS: In a cohort of returned travellers, we were able to identify multiple factors that are correlated with both aetiology and outcome of imported gastrointestinal syndromes. We predict these data will be valuable in the development of diagnostic and therapeutic pathways for patients with imported gastrointestinal infections.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".