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Record W4397029734 · doi:10.29327/1336239.30-1

TRANSTORNO DO ESPECTRO AUTISTA: ALTERAÇÕES NA MICROBIOTA E SEUS IMPACTOS GASTROINTESTINAIS

2023· article· pt· W4397029734 on OpenAlexaff
Gabriel Parga Jarpa, Milena Moreira de Medeiros, Daniel Cardoso

Bibliographic record

Venuenot available
Typearticle
Languagept
FieldMedicine
TopicChild Nutrition and Feeding Issues
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsComputer scienceChemistryAstrobiologyPhysics

Abstract

fetched live from OpenAlex

INTRODUÇÃO: O Transtorno do Espectro Autista (TEA) consiste em um distúrbio de alterações funcionais do neurodesenvolvimento podendo manifestar-se em diferentes graus em todos os grupos sociais e econômicos.Embora o Sistema Único de Saúde ofereça assistência integral aos portadores dessa deficiência, o Brasil não apresenta dados concretos referentes à sua prevalência no país.. O TEA é considerado uma disfunção do sistema nervoso central (SNC) acompanhada de alterações em diferentes órgãos e sistemas, sugerindo a presença de manifestações gastrointestinais crônicas em pacientes diagnosticados, o que, por sua vez, indicam a presença de um eixo cérebro-intestinal alterado relacionado a um desequilíbrio da microbiota intestinal se comparado a indivíduos neurotípicos.Náuseas e vômitos, constipação, quadro de obesidade, inflamações no trato digestivo e deficiência de micronutrientes, são comuns e capazes de ocasionar o agravamento do TEA podendo levar ao aumento de problemas comportamentais, frustração, irritabilidade, hétero e autoagressão, e diminuição do estado de foco.Portanto, tendo em vista o impacto social e psicológico gerado pela disbiose microbiana e suas expressões gastrointestinais, terapêuticas estão constantemente sendo desenvolvidas e estudadas visando uma melhor qualidade de vida e bem estar

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.298
Teacher spread0.272 · 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 designObservational
Domainnot available
GenreEmpirical

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
Published2023
Admission routes1
Has abstractyes

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