Responders and non-responders to probiotic interventions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As with many clinical studies, trials using probiotics have shown clearly that some patients benefit from the treatment while others do not. For example if treatment with probiotics leads to 36% cure rate of diarrhea, why did the other 64% not have the same result? The issue is important for human and indeed experimental animal studies for two main reasons: (i) Would changing the design of the study result in more subjects responding to treatment? (ii) If a subject does not respond what are the mechanistic reasons? In order to tackle the issue of responders and non-responders to therapy, a workshop was held by the International Scientific Association for Probiotics and Prebiotics (ISAPP). The outcome was four recommendations. 1. Clearly define the end goal: this could be supporting a health claim or having the highest clinical effect and impact. 2. Design the study to maximize the chance of a positive response by identifying precise parameters and defining the level of response that will be tested. 3. Base the selection of the intervention on scientific investigations: which strain(s) and/or product formulation should be used and why. 4. Carefully select the study cohort: use biological or genetic markers when available to stratify the patient population before enrollment and decide at what point intervention will provide the best outcome (for example, in acute phase of disease, or during remission, with or without use of pharmaceutical agents). By following these recommendations and selecting an appropriate primary outcome, it is hoped that clinical data will emerge in the future that expands our knowledge of which probiotics benefits which subjects and by what mechanism.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it