MétaCan
Menu
Back to cohort
Record W4386282787 · doi:10.3390/biomedicines11092427

Pollutants, including Organophosphorus and Organochloride Pesticides, May Increase the Risk of Cardiac Remodeling and Atrial Fibrillation: A Narrative Review

2023· review· en· W4386282787 on OpenAlexafffund
Ewen Le Quilliec, Alexia Fundere, Doa’a Al-u’datt, Roddy Hiram

Bibliographic record

VenueBiomedicines · 2023
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Exposure and Toxicity
Canadian institutionsUniversité de MontréalMontreal Heart Institute
FundersUniversité de MontréalInstitut de Cardiologie de MontréalCanada Foundation for Innovation
KeywordsAtrial fibrillationPesticidePollutantMedicineInternal medicineCardiologyBiologyEcology

Abstract

fetched live from OpenAlex

Atrial fibrillation (AF) is the most common type of cardiac rhythm disorder. Recent clinical and experimental studies reveal that environmental pollutants, including organophosphorus-organochloride pesticides and air pollution, may contribute to the development of cardiac arrhythmias including AF. Here, we discussed the unifying cascade of events that may explain the role of pollutant exposure in the development of AF. Following ingestion and inhalation of pollution-promoting toxic compounds, damage-associated molecular pattern (DAMP) stimuli activate the inflammatory response and oxidative stress that may negatively affect the respiratory, cognitive, digestive, and cardiac systems. Although the detailed mechanisms underlying the association between pollutant exposure and the incidence of AF are not completely elucidated, some clinical reports and fundamental research data support the idea that pollutant poisoning can provoke perturbed ion channel function, myocardial electrical abnormalities, decreased action potential duration, slowed conduction, contractile dysfunction, cardiac fibrosis, and arrhythmias including AF.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.055
GPT teacher head0.312
Teacher spread0.257 · 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.

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

Citations13
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueBiomedicinesSame topicPesticide Exposure and ToxicityFrench-language works237,207