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Record W2604078014 · doi:10.1177/1178630217700628

Neurological Deficits After Long-term Pyrethroid Exposure

2017· article· en· W2604078014 on OpenAlexfundno aff
Martin Rune Hassan Hansen, Erik Jørs, Flemming Lander, Guido Condarco, Fróði Debes, Noemí Tirado, Vivi Schlünssen

Bibliographic record

VenueEnvironmental Health Insights · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Exposure and Toxicity
Canadian institutionsnot available
FundersInstitute of GeneticsDanmarks Frie ForskningsfondAase og Ejnar Danielsens Fond
KeywordsNeurocognitivePyrethroidMedicineOdds ratioPesticideLogistic regressionEnvironmental healthToxicologyInternal medicineBiologyCognitionPsychiatry

Abstract

fetched live from OpenAlex

Pyrethroid pesticides have been suggested to be a cause of Parkinson disease and other neurodegenerative diseases. To investigate this, a cross-sectional study was conducted among 120 Bolivian public health vector program spray men, primarily exposed to pyrethroids. Pesticide exposure and central nervous system (CNS) symptoms were determined by a structured interview, whereas neuromotor and neurocognitive performance was assessed using the computerized Behavioral Assessment and Research System and CATSYS system. Individuals exposed to higher levels reported significantly more CNS symptoms (adjusted odds ratio per quintile of cumulative exposure = 2.01 [1.22-3.31]). There was no association seen between pyrethroid exposure and neuromotor performance. Higher spraying intensity was associated with significantly worse neurocognitive performance in structural equation models (adjusted β per quintile = -0.405 [-0.660 to -0.150]), and workers only exposed to pyrethroids performed worse than workers also exposed to other pesticides (adjusted β = -1.344 [-2.224 to -0.464]). Chronic pyrethroid exposure may cause deterioration in neurocognitive performance, and exposure control is recommended.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.246
Teacher spread0.221 · 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 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

Citations43
Published2017
Admission routes1
Has abstractyes

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