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Record W4380450113 · doi:10.1097/jom.0000000000002908

Blood and Hand Surface Lead in Veterinary Workers Using Lead Shielding During Diagnostic Radiography

2023· article· en· W4380450113 on OpenAlexaff
Monique N Mayer, Tongchen Feng, Sally Sukut, Sheldon Wiebe, Sarah Parker, Barry Blakley, Niels Koehncke

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy Metal Exposure and Toxicity
Canadian institutionsSaskatoon Medical ImagingUniversity of Saskatchewan
Fundersnot available
KeywordsLead (geology)MedicineElectromagnetic shieldingLead exposureLead poisoningPopulationLead apronWhole bloodSurgeryRadiation protectionEnvironmental healthNuclear medicineMaterials scienceInternal medicineComposite materialBiology

Abstract

fetched live from OpenAlex

OBJECTIVES: The objectives are to compare lead blood concentrations in veterinary workers using lead shielding with concentrations in a control population, to measure hand surface lead before and after use of shielding, and to compare hand surface lead with and without the use of disposable gloves worn under hand shielding. METHODS: Blood and hand wipe samples were analyzed for lead using inductively coupled plasma mass spectrometry. RESULTS: There was no difference in blood lead between exposed and control groups. After lead glove use, 69% (18/26) of hand surface lead samples from workers not using disposable gloves were greater than 500 μg, 42% (11/26) were greater than 1000 μg, and 12% (3/26) were greater than 2000 μg. CONCLUSIONS: If lead shielding use is unavoidable, disposable gloves should be worn, and skin should be decontaminated after use.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.284
Teacher spread0.245 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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Same venueJournal of Occupational and Environmental MedicineSame topicHeavy Metal Exposure and ToxicityFrench-language works237,207