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Air pollution is associated with brainstem auditory nuclei pathology and delayed brainstem auditory evoked potentials

2011· article· en· W2071870096 on OpenAlexaff
Lilian Calderón‐Garcidueñas, Amedeo D’Angiulli, Randy J. Kulesza, Ricardo Torres‐Jardón, Norma Osnaya Brizuela, Lina Romero, Sheyla Keefe, Lou Herritt, Diane M. Brooks, José Ávila-Ramírez, Ricardo Delgado‐Chávez, Humberto Medina‐Cortina, Luis Oscar González‐González

Bibliographic record

VenueInternational Journal of Developmental Neuroscience · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsCarleton University
FundersNational Center for Research ResourcesNational Institute of Environmental Health Sciences
KeywordsBrainstemMedicineAuditory brainstem responseAudiologyVestibular systemMeniere's diseaseHearing lossPhysiologyPathologyInternal medicineDisease

Abstract

fetched live from OpenAlex

We assessed brainstem inflammation in children exposed to air pollutants by comparing brainstem auditory evoked potentials (BAEPs) and blood inflammatory markers in children age 96.3±8.5 months from highly polluted (n=34) versus a low polluted city (n=17). The brainstems of nine children with accidental deaths were also examined. Children from the highly polluted environment had significant delays in wave III (t(50)=17.038; p<0.0001) and wave V (t(50)=19.730; p<0.0001) but no delay in wave I (p=0.548). They also had significantly longer latencies than controls for interwave intervals I-III, III-V, and I-V (all t(50)>7.501; p<0.0001), consisting with delayed central conduction time of brainstem neural transmission. Highly exposed children showed significant evidence of inflammatory markers and their auditory and vestibular nuclei accumulated α synuclein and/or β amyloid(1-42). Medial superior olive neurons, critically involved in BAEPs, displayed significant pathology. Children's exposure to urban air pollution increases their risk for auditory and vestibular impairment.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.771
Threshold uncertainty score0.602

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.034
GPT teacher head0.263
Teacher spread0.230 · 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 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

Citations80
Published2011
Admission routes1
Has abstractyes

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