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Record W2576373381 · doi:10.1542/peds.2016-2245

Targeting Environmental Neurodevelopmental Risks to Protect Children

2017· article· en· W2576373381 on OpenAlexaff
Deborah Hirtz, Carla Campbell, Bruce P. Lanphear

Bibliographic record

VenuePEDIATRICS · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy Metal Exposure and Toxicity
Canadian institutionsSimon Fraser University
FundersNational Institute of Environmental Health Sciences
KeywordsMedicineLegislationAttention deficit hyperactivity disorderHarmPsychiatryAgency (philosophy)Environmental healthLaw

Abstract

fetched live from OpenAlex

* Abbreviations: AAP — : American Academy of Pediatrics ADHD — : attention-deficit/hyperactivity disorder EPA — : Environmental Protection Agency PBDE — : polybrominated diphenyl ether TENDR — : Targeting Environmental Neuro-Developmental Risks Pregnant women, infants, and children are continually exposed to chemicals that are toxic to brain development. Yet too little has been done to protect them from the possibility of harm. In 2015, a diverse group of physicians and other health professionals, scientists, and advocates established Project Targeting Environmental Neuro-Developmental Risks (TENDR) to focus awareness and advocate for action against toxic chemicals that contribute to the risk of development of brain-based disorders in children, including intellectual and learning disabilities, autism, and attention-deficit/hyperactivity disorder (ADHD).1 Ten years ago, this landmark agreement among leading scientists and health professionals would not have been possible, but the accumulated evidence, which illustrates a pattern of toxicity, is credible and convincing. The release of the TENDR consensus statement coincided with the recent signing into law of the Frank R. Lautenberg Chemical Safety for the 21st Century Act. This act is the first update of the Toxic Substances Control Act since the law was adopted in 1976. This legislative effort was an important step toward protecting children from toxic chemicals, but, by itself, provides too little action at too slow a pace. Specific information is given below on how health care providers can respond to this legislation and advocate for safe policies that protect children. Additional information is also provided on advice providers can give to families regarding avoidance of toxic chemicals. The etiology of neurodevelopmental disorders is complex and multifactorial, but epidemiologic data, along with laboratory studies of animals, clearly indicate that exposures to certain toxic chemicals, even at very … Address correspondence to Deborah Hirtz, MD, FAAN, Department of Pediatrics and Neurology, HSRF 426, 149 Beaumont Ave, Burlington, VT 05405. E-mail: deborah.hirtz{at}uvmhealth.org

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
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 science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.004

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 head0.019
GPT teacher head0.253
Teacher spread0.234 · 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; both teacher heads agree on what is shown here.

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

Citations5
Published2017
Admission routes1
Has abstractyes

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