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Record W2766127498 · doi:10.1177/0960327117737146

Historical documentation of lead toxicity prior to the 20th century in English literature

2017· review· en· W2766127498 on OpenAlexaff
ME Jonasson, Reza Afshari

Bibliographic record

VenueHuman & Experimental Toxicology · 2017
Typereview
Languageen
FieldEnvironmental Science
TopicHeavy Metal Exposure and Toxicity
Canadian institutionsBC Centre for Disease ControlUniversity of British ColumbiaSimon Fraser University
Fundersnot available
KeywordsLead poisoningDocumentationLead (geology)Context (archaeology)MedicineLead intoxicationEnvironmental healthLead exposureHistoryPsychiatryArchaeologyBiologyComputer science

Abstract

fetched live from OpenAlex

Lead is a heavy metal that remains a persistent environmental toxin. Although there have been a substantial number of reviews published on the health effects of lead, these reviews have predominantly focused on recent publications and rarely look at older, more historical articles. Old documents on lead can provide useful insight in establishing the historical context of lead usage and its modes of toxicity. The objective of this review is to explore historical understandings and uses of lead prior to the 20th century. One hundred eighty-eight English language articles that were published before the year 1900 were included in this review. Major themes in historical documentation of lead toxicology include lead's use in medical treatments, symptoms of lead poisoning, treatments for lead poisoning, occupational lead poisonings, and lead contamination in food and drinking water. The results of this review indicate that lead's usage was widespread throughout the 19th century, and its toxic properties were well-known. Common symptoms of lead poisoning and suggested treatments were identified during this time period. This review provides important insight into the knowledge and uses of lead before the 20th century and can serve as a resource for researchers looking at the history of lead.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.069
GPT teacher head0.375
Teacher spread0.306 · 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.

Study designNot applicable
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

Citations40
Published2017
Admission routes1
Has abstractyes

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