Lack of Effect of Lactose Digestion Status on Baseline Fecal Microflora
Bibliographic record
Abstract
BACKGROUND: The genetics of intestinal lactase divide the world's population into two phenotypes: the ability (a dominant trait) or inability (a recessive trait) to digest lactose. A prebiotic effect of lactose may impact the colonic flora of these phenotypes differently. OBJECTIVE: To detect and evaluate the effects of lactose on subjects divided according to their ability to digest lactose. METHODS: A total of 57 healthy maldigesters (n=30) and digesters (n=27) completed diet questionnaires, genetic and breath hydrogen testing, and quantitative stool analysis for species of bacteria. Log10 transformation of bacterial counts was compared with lactose intake in both groups using multiple regression analysis. RESULTS: There was a significant relationship between genetic and breath hydrogen tests. Daily lactose intake was marginally lower in lactose maldigesters (median [interquartile range] 12.2 g [31 g] versus 15 g [29.6 g], respectively). There was no relationship between lactose intake and breath hydrogen tests in either group. There were no differences in bacterial counts between the two groups, nor was there a relationship between bacterial counts and lactose intake in either group. CONCLUSION: The differential bacterial effects of lactose were not quantitatively detected in stool samples taken in the present study.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".