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Record W4220669129 · doi:10.3390/psychiatryint3010009

Gut Microbiota Research in Bipolar Disorder and Possible Implications for Precision Psychiatry: A Systematic Review

2022· review· en· W4220669129 on OpenAlexaff
Pasquale Paribello, Federica Pinna, Bernardo Carpiniello, Mirko Manchia

Bibliographic record

VenuePsychiatry International · 2022
Typereview
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsDalhousie University
Fundersnot available
KeywordsBipolar disorderPrecision medicineObservational studyPsychiatryMEDLINEScope (computer science)Gut floraMedicinePsychologyMoodComputer scienceBiologyPathologyImmunology

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.739
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.088
GPT teacher head0.447
Teacher spread0.359 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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