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Record W2157323268 · doi:10.1002/xrs.2607

X‐ray fluorescence of archived bone samples: are raised Pb levels a chance finding or an association with Paget's disease?

2015· article· en· W2157323268 on OpenAlexafffund
Lesley M Egden, Khanh Nguyen, David R. Chettle, Richard Butler, Michael Inskip, Colin E. Webber

Bibliographic record

VenueX-Ray Spectrometry · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy Metal Exposure and Toxicity
Canadian institutionsMcMaster UniversityMcMaster University Medical Centre
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSkullFemurBone mineralDiseaseHuman boneMedicineLead (geology)Bone diseasePaget's disease of bonePathologyDentistryAnatomyChemistrySurgeryBiologyOsteoporosisBiochemistry

Abstract

fetched live from OpenAlex

The K‐shell X‐ray fluorescence technique that was developed for measurement of lead in bone in living human subjects was applied to measurement of lead in archived human bone samples. The skull from a person who had suffered from Paget's disease was found to have lead concentrations of 97.5±3.6–532.2±3.2 µg Pb/g bone mineral. A femur sample from a different person, who had also suffered from Paget's disease, had a lead concentration of 18–43.5 with uncertainties between 1.6 and 2.0 µg Pb/g bone mineral. The skull lead concentrations were highly raised compared with findings from in vivo bone Pb measurements, whereas the femur lead concentrations were somewhat raised. In the past, the hypothesis has been put forward that lead can be involved in the etiology of Paget's disease; however, other reports in the literature have not supported that hypothesis. The current findings, while intriguing, are too few to be in any way conclusive but merit further study. Copyright © 2015 John Wiley & Sons, Ltd.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.050
GPT teacher head0.267
Teacher spread0.217 · 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
Published2015
Admission routes2
Has abstractyes

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