MétaCan
Menu
Back to cohort
Record W3012897309 · doi:10.1186/s12940-020-0567-2

Prenatal exposure to legacy contaminants and visual acuity in Canadian infants: a maternal-infant research on environmental chemicals study (MIREC-ID)

2020· article· en· W3012897309 on OpenAlexafffundabout
C. Polevoy, Tye E. Arbuckle, Youssef Oulhote, Bruce P. Lanphear, Kevin A. Cockell, Gina Muckle, Dave Saint‐Amour

Bibliographic record

VenueEnvironmental Health · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversité de MontréalUniversité LavalBC Children's HospitalSimon Fraser UniversityHealth CanadaCentre Hospitalier Universitaire Sainte-JustineUniversité du Québec à Montréal
FundersHealth Canada
KeywordsVisual acuityMERCURY EXPOSUREConfoundingMedicinePregnancyMercury (programming language)PhysiologyEnvironmental healthEnvironmental chemistryInternal medicineBiomonitoringOphthalmologyChemistry

Abstract

fetched live from OpenAlex

BACKGROUND: Prenatal exposure to environmental contaminants can have deleterious effects on child development. While psychomotor, cognitive and behavioural outcomes have been investigated in relation to chronic exposure, the associations with visual functions remains unclear. The present study's aim was to assess the associations of prenatal exposure to legacy persistent organic pollutants and heavy metals with visual acuity in Canadian infants. The potential protective effects of selenium against mercury toxicity were also examined. METHODS: Participants (mean corrected age = 6.6 months) were part of the Maternal-Infant Research on Environmental Chemicals (MIREC) study. Concentrations of polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), lead and mercury were measured in maternal blood during pregnancy, as well as in the cord blood. The Teller acuity card test (TAC) (n = 429) and the visual evoked potentials in a sub-group (n = 63) were used to estimate behavioural and electrophysiological visual acuity, respectively. Multivariable linear regression models were used to investigate the relationship between exposure to each contaminant and visual acuity measures, while controlling for potential confounders. Breastmilk selenium, which was available for about half of the TAC and VEP samples, was also taken into account in the mercury models as exploratory analyses. RESULTS: We observed no significant associations between exposure to any contaminants and TAC. Analyses revealed a negative trend (p values < 0.1) between cord blood lead and mercury and electrophysiological visual acuity, whereas PCB and PBDE showed no association. When adding breastmilk selenium concentration to the mercury models, this association became statistically significant for cord concentrations (β = - 3.41, 95% CI = - 5.96,-0.86), but also for blood levels at 1st and 3rd trimesters of pregnancy (β = - 3.29, 95% CI = - 5.69,-0.88). However, further regression models suggested that this change in estimates might not be due to adjustment for selenium, but instead to a change in the study sample. CONCLUSIONS: Our results suggest that subtle, but detectable alterations of infant electrophysiological visual acuity can be identified in a population prenatally exposed to low mercury concentrations. Compared to behavioural visual acuity testing, electrophysiological assessment may more sensitive in detecting visual neurotoxicity in relation with prenatal exposure to mercury.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.016
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.039
GPT teacher head0.355
Teacher spread0.316 · 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

Citations23
Published2020
Admission routes3
Has abstractyes

Explore more

Same venueEnvironmental HealthSame topicMercury impact and mitigation studiesFrench-language works237,207