Predictors of Small Intestinal Bacterial Overgrowth in Symptomatic Patients Referred for Breath Testing
Bibliographic record
Abstract
Background: Indications for a breath test (BT) are well established in the symptomatic patient with risk factors predisposing them to small intestinal bacterial overgrowth (SIBO). Characteristics and the profile of this population are not well known. Our objective was to study the characteristics of patients undergoing a BT for SIBO and to identify factors associated with a positive BT. Methods: Retrospective study was conducted from 2012 to 2016 at the neurogastroenterology unit of the Centre Hospitalier de l’Universite de Montreal (CHUM). All patients who completed a BT (lactulose and/or glucose) were included. Demographics and clinical factors were analyzed to identify predictors of positive BT. Type of antibiotic treatment and clinical response were compiled. Groups of patients with (SIBO + ) and without SIBO (SIBO - ) were also compared. Results: A total of 136 patients were included in the study (mean age 51.2, range 20 - 80 years; 63% women), and SIBO was detected in 33.8% (n = 46). Both groups were similar in terms of age, body mass index, and gender. SIBO was significantly associated with the presence of abdominal pain (odds ratio (OR) = 4.78; P < 0.05), bloating (OR = 5.39; P < 0.05), smoking (OR = 6.66; P < 0.05), and anemia (OR = 4.08; P < 0.05). No association was identified with gender, age, obesity, and risk factors for SIBO. Antibiotics were used in 43% of patients with a positive BT, but clinical response was not significantly different in the subgroup that received antibiotics versus the subgroup that did not. Conclusions: The prevalence of SIBO is high in symptomatic patients who underwent breath testing. Abdominal pain, bloating, smoking, and anemia are strongly associated with SIBO. Treatment of SIBO with antibiotics needs to be further investigated to better determine its efficacy on gastrointestinal symptoms. J Clin Med Res. 2020;12(10):655-661 doi: https://doi.org/10.14740/jocmr4320
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".