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Dental Amalgam and Multiple Sclerosis: A Systematic Review and Meta‐Analysis

2007· review· en· W2001700012 on OpenAlexaff
Kevin Aminzadeh, Mahyar Etminan

Bibliographic record

VenueJournal of Public Health Dentistry · 2007
Typereview
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsBurnaby Hospital
Fundersnot available
KeywordsAmalgam (chemistry)MedicineMeta-analysisCochrane LibraryOdds ratioDentistryMEDLINEMultiple sclerosisSystematic reviewMercury (programming language)PsychiatryPathologyComputer science

Abstract

fetched live from OpenAlex

OBJECTIVES: Amalgam restorations have long been controversial due to their mercury content. Allegations that the mercury may be linked to nervous disorders such as Alzheimer's, chronic fatigue syndrome, and multiple sclerosis (MS) have fueled the calls for the removal of amalgam restorations from dentists' armamentarium. To explore and quantify the association between amalgam restorations and MS we have conducted a systematic review and meta-analysis of the literature. METHODS: A systematic search in Medline (from 1966 to April 2006), EMBASE (2006, Week 16), and the Cochrane library (Issue 2, 2006) for English-language articles meeting specific definitions of MS and amalgam exposure was conducted. Studies were also identified using the references of retrieved articles. Studies were independently reviewed by two authors and disagreements were resolved by consensus. Studies were selected based on an a priori of defined criteria. Odds ratios (ORs) or relative risks were pooled using the random effects model. Heterogeneity was assessed using Q statistics. RESULTS: The pooled OR for the risk of MS among amalgam users was consistent, with a slight, nonstatistically significant increase between amalgam use and risk of MS. CONCLUSION: Future studies that take into consideration the amalgam restoration size and surface area along with the duration of exposure are needed in order to definitively rule out any link between amalgam and MS.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.867
Threshold uncertainty score0.929

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.308
GPT teacher head0.417
Teacher spread0.109 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations57
Published2007
Admission routes1
Has abstractyes

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