Critical Evaluation of Diagnosing Bacterial Overgrowth in the Proximal Small Intestine
Bibliographic record
Abstract
BACKGROUND: Clinical small bowel bacterial overgrowth (SBBO) syndrome can be objectified by bacterial overgrowth tests. As direct culture of jejunal aspirates has disadvantages, noninvasive tests such as breath tests (BTs) are used. Major drawback of lactulose BT might be rapid lactulose transit to the colon. We evaluated diagnosing bacterial overgrowth using experimental and standard BT, and culture and molecular-based methods. STUDY: Bacterial overgrowth was analyzed in 11 controls and 15 SBBO predisposed subjects. During experimental breath testing, an occlusive balloon limited lactulose to the small intestine. Jejunal fluid was analyzed using culture and molecular-based methods. Bacterial overgrowth was diagnosed on the basis of 20 ppm hydrogen or methane increase above baseline within 90 minutes or more than 10 CFU/mL excluding lactobacilli and streptococci and furthermore using all published definitions. RESULTS: Experimental and standard BT showed no changes in timing of hydrogen excretion between controls and SBBO subjects. Using standard BT, 3/11 controls and 8/15 SBBO subjects were bacterial overgrowth positive. Total counts showed no significant differences between controls and SBBO subjects using culture and molecular-based methods. Bacterial overgrowth was diagnosed in 0/9 controls and 4/12 SBBO subjects using culture-based methods. Other definitions used in literature revealed no significant differences between controls and SBBO subjects. CONCLUSIONS: In a small group of subjects, the experimental BT did not improve the ability of lactulose BT to diagnose bacterial overgrowth. Culturing showed less bacterial overgrowth in controls compared with BT. Remarkably, current diagnostic criteria do not seem to be accurate in discriminating between SBBO subjects and controls.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".