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Record W2996190508 · doi:10.1088/1361-6579/ab6019

Elemental analysis in living human subjects using biomedical devices

2019· review· en· W2996190508 on OpenAlexaff
David R. Chettle, Fiona E. McNeill

Bibliographic record

VenuePhysiological Measurement · 2019
Typereview
Languageen
FieldHealth Professions
TopicRadioactivity and Radon Measurements
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRisk analysis (engineering)Elemental analysisComputer scienceHuman healthNeutron activation analysisSystems engineeringData scienceMedicineMedical physicsEngineeringEnvironmental healthChemistry

Abstract

fetched live from OpenAlex

Today, patients undergoing dialysis are at low risk for aluminum-induced dementia. Workers are unlikely to experience cadmium-induced emphysema and the public's exposure to lead is an order of magnitude lower than in 1970. The research field of in vivo elemental analysis has played a role in these occupational and environmental health improvements by allowing the effects of people's chronic exposure to elements to be studied using non-invasive, painless, and relatively low-cost technology. From the early 1960s to the present day, researchers have developed radiation-based systems to measure the elemental content of organs at risk or storage organs. This reduces the need for (sometimes painful) biopsy and the risk of infection. Research and development has been undertaken on forty-nine in vivo measurement system designs. Twenty-nine different in vivo elemental analysis systems, measuring 22 different elements, have been successfully taken from design and testing through to human measurement. The majority of these systems employ either neutron activation analysis or x-ray fluorescence analysis as the basis of the measurement. In this review, we discuss eight of the successful systems, explaining the rationale behind their development, the methodology, the health data that has resulted from application of these tools, and provide our opinion on potential future technical developments of these systems. We close by discussing four technologies that may lead to new directions and advances in the whole field.

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 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 score0.774
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.593
GPT teacher head0.529
Teacher spread0.064 · 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

Citations11
Published2019
Admission routes1
Has abstractyes

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