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Record W4414624744 · doi:10.1186/s12919-025-00345-1

International fluoride symposium: effects of fluoride on human health and its mechanisms of action – a meeting report

2025· article· en· W4414624744 on OpenAlexaffabout
E. Angeles Martínez‐Mier, Gina A. Castiblanco, Guillermo Tamayo‐Cabeza, Andréa Aguiar, Morteza Bashash, Kelly J. Brunst, Carrie V. Breton, Alejandra Cantoral, J.L. Figueroa, Carly Goodman, Howard Hu, Jesus Ibarluzea-Maurolagoitia, Bruce P. Lanphear, Ashley J. Malin, Karen E. Peterson, Elizabeth Roberts, Susan L. Schantz, Mikel Subiza‐Pérez, Marcela Tamayo‐Ortiz, Martha María Téllez‐Rojo, Christine Till, Deborah J. Watkins, Frank Lippert

Bibliographic record

VenueBMC Proceedings · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicFluoride Effects and Removal
Canadian institutionsSimon Fraser UniversityYork University
FundersNational Institute of Environmental Health Sciences
KeywordsFluorideHuman healthCohortHuman studiesWater fluoridationCohort studyPublic health

Abstract

fetched live from OpenAlex

Fluoride prevents dental caries in a dose-response manner, leading some countries to adjust fluoride levels in water or table salt, as well as to promote the widespread use of topical fluoride. Recent studies have found associations between prenatal fluoride exposure levels of < 1.5 mg/L in water and urine and adverse neurodevelopmental outcomes. Although high fluoride levels have been recognized as neurotoxic in the past, a large body of contemporary evidence derived from retrospective analyses of birth cohort studies suggests fluoride may be neurotoxic to children at lower levels, highlighting the need for further, prospective research and multidisciplinary collaborations. The International Fluoride Symposium, held from April 29 to 30, 2024, brought together 20 researchers from the United States, Canada, Mexico, and Spain to discuss the impacts of fluoride on human health and its mechanisms of action. The primary goals of the symposium were to address challenges related to assessing fluoride exposure, share findings from cohort studies, develop a comprehensive research agenda, and foster international research partnerships. Key discussions included the dental caries preventive and toxic effects of fluoride, sources of fluoride exposure, biomarkers, dietary intake assessment methods, and analytical challenges. Presentation of results from cohort studies highlighted research on prenatal fluoride exposure and its association with neurodevelopmental outcomes and presented perspectives for future analyses. The symposium emphasized the need for customized dietary fluoride intake assessment tools, the development of high-throughput analytical methods for fluoride analysis, and research on the combined effects of fluoride with other chemical elements commonly found in the environment and the human diet. Additionally, there was a call for the harmonization of cohort data from diverse populations to address urgent questions about the impact of fluoride on human neurodevelopment and other health outcomes beyond oral health. It was agreed that prospective longitudinal cohort studies intentionally designed to assess fluoride exposure and neurodevelopment are essential, as none of the existing birth cohorts were designed to specifically study fluoride exposure (e.g., selection of biomarkers, collection intervals, diet exposure assessment). Furthermore, broader environmental health cohort studies that incorporate high-quality biomonitoring of waterborne neurotoxicants (such as fluoride, arsenic, lead, mercury), repeated measures of exposure, and inclusion of key covariates (e.g., socio-economic status, diet, iodine) are encouraged. Finally, developing effective communication strategies among scientists and the public was considered crucial for advancing fluoride research and mitigating potential health risks.

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)0.007
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.005
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0030.001
Scholarly communication0.0060.006
Open science0.0020.006
Research integrity0.0100.009
Insufficient payload (model declined to judge)0.0190.009

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 head0.014
GPT teacher head0.287
Teacher spread0.273 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2025
Admission routes2
Has abstractyes

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