Microbiota in Functional Gastrointestinal Disorders in Infancy: Implications for Management
Bibliographic record
Abstract
The complex and diverse intestinal microbiome is recognized as important in promoting human health. An altered gut microflora, referred to as dysbiosis, is increasingly recognized as having an etiologic role in a variety of conditions, including functional gastrointestinal disorders: colic in infants and irritable bowel syndrome in older children. Probiotics are defined as live microorganisms that, if ingested in sufficient amounts, restore microbial homeostasis and have a benefit on health. Randomized controlled trials indicate that probiotics can be effective in a variety of intestinal conditions, including colic and irritable bowel syndrome. Probiotics may promote gut microbial diversity, but timing of the intervention appears crucial. Strain-specific effects on colonization resistance, epithelial barrier integrity, modulation of signal transduction, impacts on innate and adaptive immune responses, and effects on visceral hyperalgesia likely explain the observed variability in various probiotic strains. In the future, probiotics are likely to be chosen for use in a defined clinical setting based on underlying mechanism(s) of action. The precise component of the probiotic agent mediating observed effects is the subject of current research. Unresolved issues relate to optimal dosages, timing of ingestion, single versus combination formulations, maintenance of viability in storage, and the merits of employing probiotic-derived products.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".