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Impacto da exposição crônica a poluentes no risco de infarto do miocárdio e AVC: Uma revisão atualizada de literatura

2025· article· pt· W4407595010 on OpenAlexaff
L. Oliveira, Kalyandra Imperatriz Santos Ramos, João Victor de Matos Caetano, G. Carvalho, Larissa Pereira, Vitória Muniz Assunção Moreira, João Thales Vasconcelos Martins, Willas Ferreira Furtado, Clarice Terranova Agostinho

Bibliographic record

VenueBrazilian Journal of Implantology and Health Sciences · 2025
Typearticle
Languagept
FieldAgricultural and Biological Sciences
TopicAgricultural and Food Sciences
Canadian institutionsImpact
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

As doenças cardiovasculares, como infarto do miocárdio e acidente vascular cerebral (AVC), estão entre as principais causas de morbimortalidade mundial. Este estudo realizou uma revisão integrativa da literatura sobre o impacto da exposição crônica a poluentes atmosféricos, como material particulado fino (PM2.5), óxidos de nitrogênio (NOx) e dióxido de enxofre (SO2), no risco dessas enfermidades. Os resultados indicaram que os poluentes desencadeiam processos fisiopatológicos, incluindo inflamação sistêmica, estresse oxidativo e disfunção endotelial, os quais estão diretamente associados ao desenvolvimento de doenças cardiovasculares. Além disso, a interação com fatores genéticos e ambientais, como ruído urbano e variações climáticas extremas, amplifica os efeitos adversos. Constatou-se, ainda, que a presença de áreas verdes em centros urbanos pode mitigar esses riscos, reforçando a importância de políticas públicas para a redução da poluição e promoção da saúde cardiovascular. Sugere-se o desenvolvimento de estratégias preventivas e a ampliação de estudos que explorem as interações ambientais e genéticas.

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)0.005
metaresearch head score (Gemma)0.012
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 score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0070.007
Science and technology studies0.0010.003
Scholarly communication0.0060.004
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.038
GPT teacher head0.344
Teacher spread0.306 · 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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