Symposium 15: Impact of Environmental Contaminants on Child Neurodevelopment
Bibliographic record
Abstract
Summary Abstract: Children are exposed to toxic chemicals throughout development and the long-term consequences of this exposure can be profound. Despite decades of research documenting the vulnerability of the developing brain to environmental contaminants, there has been little progress in protecting against developmental neurotoxicity. This symposium will discuss recent research in developmental neurotoxicology using a "developmental origins of health and disease" (DOHaD) framework that examines the context in which environmental contaminants exert their effects. We will examine the timescale for developmental toxicity, windows of vulnerability, and the bases of individual differences in vulnerability, including sex-specific effects of chemical exposures. This symposium will feature new pregnancy and birth cohort studies that have implicated fluoride as a developmental neurotoxin and endocrine disruptor. In addition, we will discuss emerging issues in epidemiology, including how environmental contaminants may interact with non-chemical stressors and have lifelong impacts on cognition and behaviours. This symposium will be capped with a discussion of the public's knowledge, attitudes, and behaviours related to developmental toxicity and strategies to reduce exposure. All speakers will be asked to draw conclusions on research priorities, and discuss how to balance regulators' need for "ideal evidence" with a public health strategy that aims to protect the public from critical environmental hazards. The symposium will consist of the following five presentations, each 12 minutes in length, followed by a 15 minute discussion. 1. John Krzeckowski, PhD, York University, Toronto, Canada. TITLE: Applying a Dimensional Framework to the Study of Developmental Neurotoxicity 2. Carly Goodman, PhD candidate, York University, Toronto, Canada TITLE: Sex difference of Developmental Neurotoxicants on Intellectual abilities: A systematic review and meta-analysis 3. Meaghan Hall, PhD candidate, York University, Toronto, Canada. TITLE: Fluoride Exposure and Hypothyroidism in Pregnant Women: A Potential Mechanism of Fluoride Neurotoxicity 4. Ashley Malin, PhD, University of Florida, Florida, USA. TITLE: Urinary Fluoride Levels and Metal Co-Exposures among Pregnant Women in Los Angeles, California 5. Rivka Green, PhD, The Hospital for Sick Children, Toronto, Canada. TITLE: Translating developmental neurotoxicity for the public: A large, multi-country, randomized-control trial investigating children's environmental health literacy
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".