Preferred Reporting Items for Microbiotherapy (PRIM) Guidelines Across Medical Disciplines: An International Delphi Consensus
Bibliographic record
Abstract
Microbiotherapy has opened new avenues for managing dysbiosis-related diseases. However, many studies did not cover all the necessary reporting items for microbiotherapy making the interpretation of results, safety assessment, technology extension, and even the transparency of legitimacy difficult. This project consisted of 2 phases. First, we proposed an initial preferred reporting items for microbiotherapy (PRIM) checklist and applied it to oncology studies from 2011 to 2023 according to Meta-Analyses guideline. Only 39.3% (n = 64) of these studies (n = 163) met all PRIM checklist items. The culture-based microbiotherapy (CMT) studies had higher score than non-culture-based (NMT) ones (p = 0.018). In the second phase, the expert panel consisting of 22 specialists from eight countries across Asia, Australia, Europe, and North America refined and finalized the PRIM guidelines (named as PRIM 2024) through Delphi consensus. The PRIM 2024 guidelines conclude 10 statements and 18 points on diagnosis, delivery route, source, classification, preparation, dosage, state, concomitant treatment, efficacy, and safety. The panel defined less than 80% of all PRIM points (14 points) as low-quality reports. These guidelines are recommended for reporting on microbiotherapy in clinical studies and reports on compassionate use, including but not limited to fecal microbiota transplantation, phage therapy, probiotics, and synbiotics. These consistent and transparent reporting items can help researchers and practitioners better evaluate, compare, implement research findings in microbiotherapy.
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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 | 0.506 | 0.501 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".