Guide to designing, conducting, publishing, and communicating results of clinical studies involving probiotic applications in human participants
Bibliographic record
Abstract
The heterogeneity of human clinical trials to assess the effectiveness of probiotics presents challenges regarding interpretation and comparison. Evidence obtained from clinical trials among a population with a disease or specific risk factors may not be generalizable to healthy individuals. The evaluation of interventions in healthy persons requires careful selection of outcomes due to the absence of health indicators and the low incidence of preventable conditions. Given the tremendous resources invested in such trials, development of consistent approaches to assessing the effectiveness of probiotics would be beneficial. Furthermore, the reporting, presentation and communication of results may also affect the validity of the scientific evidence obtained from a trial. This review outlines the challenges associated with the design, implementation, data analysis and interpretation of clinical trials in humans involving probiotics. Best practices related to their design are offered along with recommendations for enhanced collaboration to advance research in this emerging field.
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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 | 0.118 | 0.241 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.100 | 0.120 |
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".