Bibliographic record
Abstract
Probiotics have been explored in an exponentially increasing number of clinical trials for their health effects. Drawing conclusions from the published literature for the medical practitioner is difficult since rarely more than two clinical trials were conducted with the same probiotic strain against the same medical condition. Consequently, the European Society for Paediatric Gastroenterology, Hepatology and Nutrition (ESPGHAN) made a few recommendations restricting it to probiotic use against acute gastroenteritis and antibiotic-associated diarrhea. Recent studies also made a strong case for probiotic use against sepsis in preterm and term infants from developing countries. Conclusions on the value of probiotics are best based on detailed meta-analyses (MA) of randomized controlled trials (RCT). Outcomes of MA are discussed in the present review for a number of gastroenterology conditions. Since these MA pool data from trials using different probiotic species, large RCT published sometimes come to different conclusions than MA including these studies. This is not necessarily a contradiction but may only mean that the specific probiotic species did not work under the specified conditions. Positive or negative generalization about probiotics and prebiotics should be avoided. Credible effects are those confirmed in independent trials with a specified probiotic strain or chemically defined prebiotic in a specified patient population under the specified treatment conditions. Even distinct technological preparations of the same probiotic strain might affect clinical outcomes if they alter bacterial surface structures. Underpowered clinical trials are another problem in the probiotic field. Data obtained with sophisticated omics technologies, but derived from less than ten human subjects should be interpreted with caution even when published in high impact journals.
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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".