Small intestinal bacterial overgrowth in systemic sclerosis
Bibliographic record
Abstract
OBJECTIVES: The aims of this study were to: (i) determine the prevalence of small intestinal bacterial overgrowth (SIBO) in unselected patients with SSc; (ii) assess both clinical presentation and outcome of SIBO; and (iii) make predictions about which SSc patients are at risk for SIBO. METHODS: Fifty-one consecutive patients with SSc underwent glucose hydrogen and methane (H(2)/CH(4)) breath test. All SSc patients also completed a questionnaire for intestinal symptoms, and a global symptomatic score (GSS) was calculated. SSc patients with SIBO were given rotating courses of antibiotics (norfloxacin/metronidazole) for 3 months; glucose H(2)/CH(4) breath test was performed at 3-month follow-up. RESULTS: The prevalence of SIBO was 43.1% in our SSc patients. After logistic regression, we identified the following risk factors for SIBO: presence of diarrhoea and constipation. Interestingly, we observed a marked correlation between values of GSS of digestive symptoms (> or =5) and the presence of SIBO (P = 10(-6)); indeed, both sensitivity and specificity of GSS > or =5 to predict SIBO were as high as 0.909 and 0.862, respectively. Finally, eradication of SIBO was obtained in 52.4% of the SSc patients with a significant improvement of intestinal symptoms. CONCLUSION: Our study underscores that SIBO often occurs in SSc patients. We further suggest that GSS may be systematically performed in SSc patients; since we found a correlation between GSS of digestive symptoms > or =5 and SIBO, we suggest that glucose H(2)/CH(4) breath test may be performed in the subgroup of SSc patients exhibiting GSS > or =5.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".