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Record W2096744161 · doi:10.1002/ajim.20737

Bone manganese as a biomarker of manganese exposure: A feasibility study

2009· article· en· W2096744161 on OpenAlexafffund
Ana Pejović‐Milić, Aslam, David R. Chettle, John Oudyk, Michael W Pysklywec, Ted Haines

Bibliographic record

VenueAmerican Journal of Industrial Medicine · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy Metal Exposure and Toxicity
Canadian institutionsCanada Auto WorkersMcMaster UniversityToronto Metropolitan University
FundersMcMaster University
KeywordsMedicineManganeseBiomarkerOccupational exposureNuclear medicineInternal medicineEnvironmental healthMetallurgy

Abstract

fetched live from OpenAlex

BACKGROUND: There is a need for a diagnostic tool with the ability to measure cumulative exposure to manganese (Mn) in the workplace. Measuring bone Mn levels with in vivo neutron activation analysis (IVNAA) could serve as a biomarker of past exposure. Bone Mn levels of welders were measured and compared to the levels found in subjects without exposure to the element. METHOD: Forty subjects (30 welders and 10 controls) were recruited. An occupational history was obtained and subjects underwent IVNAA bone Mn measurements. RESULTS: The mean bone Mn levels were (2.9 +/- 0.4) and (0.1 +/- 0.7) microg Mn/g Ca for welders and controls, respectively (P < 0.05). CONCLUSIONS: This project, the first of its kind, reports differences in bone Mn between Mn-exposed welders and non-occupationally exposed subjects. It appears that bone Mn levels do reflect differences in the occupational exposure of welders.

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)0.003
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 score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.049
GPT teacher head0.316
Teacher spread0.267 · 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

Citations41
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueAmerican Journal of Industrial MedicineSame topicHeavy Metal Exposure and ToxicityFrench-language works237,207