Gut Microbiota Research in Bipolar Disorder and Possible Implications for Precision Psychiatry: A Systematic Review
Bibliographic record
Abstract
Bipolar disorder (BD) is a highly disabling condition with a chronic and relapsing nature. Despite the substantial socioeconomic burden associated with BD, there are still significant research gaps in risk stratification, diagnostic accuracy, and treatment selection, all key components of precision psychiatry. One possible strategy to increase the validity of precision psychiatry approaches in BD is to increase our knowledge of disorder-associated gut microbiota perturbations. To this end, we systematically reviewed the evidence on gut microbiota alterations in relation to precision psychiatry approaches on BD. We performed a systematic review on PubMed/MEDLINE and Web of Science to identify original articles investigating the possible clinical applications of microbiota analyses for pragmatic precision psychiatry in BD. A pearl growing strategy was employed to enlarge the scope of this review. The primary search strategy yielded one paper and an additional one was identified through reference tracking. The included studies were observational, with one study of good quality. The identified results justify the efforts devolved in this area of research and underscore the need to expand these investigations through additional larger and properly designed studies.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".