Guidelines for best practice in placebo-controlled experimental studies on probiotics in rodent animal models
Bibliographic record
Abstract
In the absence of established best practice standards in the probiotic field for reducing the risk of bacterial transfer between experimental groups, we developed protocols and methods to ensure the highest quality and interpretability of results from animal studies, even when performed in non-conventional animal care facilities. We describe easily implementable methods for reducing cross-contamination during animal housing, behavioural testing, and euthanasia, along with highlighting protocols for contamination detection in experimental subjects and laboratory areas using qPCR. In light of the high cross-contamination risks between animals during experiments involving probiotics, constant vigilance in animal care and research protocols is critical to ensure valid and reliable research findings.
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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 | 0.199 | 0.194 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.010 | 0.003 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".