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Record W4411334103 · doi:10.55905/oelv23n6-087

Influência da microbiota intestinal no desenvolvimento de doenças neurodegenerativas: uma revisão sistemática

2025· article· pt· W4411334103 on OpenAlexaboutno aff
Izabella de Souza Rabelo, Gabriel Adan Araújo Leite, Lorena de Oliveira Tannus, Tuane Carolina Ferreira Moura, Luciana Constantino Silvestre, Fernanda Póvoas dos Anjos, Amanda da Costa Silveira Sabbá

Bibliographic record

VenueOBSERVATÓRIO DE LA ECONOMÍA LATINOAMERICANA · 2025
Typearticle
Languagept
FieldMedicine
TopicChild Nutrition and Feeding Issues
Canadian institutionsnot available
Fundersnot available
KeywordsMicrobiologyBiologyMedicine

Abstract

fetched live from OpenAlex

Objetivo: Analisar a relação entre a microbiota intestinal e o desenvolvimento de doenças neurodegenerativas. Método: Revisão sistemática conduzida seguindo as recomendações PRISMA, com buscas nas bases MEDLINE, PUBMED e LILACS até agosto de 2024. Foram incluídos estudos de caso-controle e coorte, avaliados pela ferramenta Newcastle-Ottawa (NOS). Dezesseis artigos foram selecionados para análise. Resultados: Os estudos indicaram diferenças significativas na microbiota de indivíduos acometidos, incluindo aumento do filo Bacteroidetes e de bactérias pró-inflamatórias, além de redução de precursores de substâncias que mantêm a integridade imunológica e regulam a permeabilidade intestinal. Algumas doenças, como a Doença de Parkinson, apresentam sintomas iniciais no trato gastrointestinal, sugerindo a contribuição da disbiose para essas condições. Conclusão: A disbiose intestinal pode ser um fator de risco para doenças neurodegenerativas, reforçando a necessidade de mais estudos para explorar essa associação.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.015
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.038
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0140.012
Science and technology studies0.0010.002
Scholarly communication0.0070.004
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.020
GPT teacher head0.295
Teacher spread0.275 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2025
Admission routes1
Has abstractyes

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