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Record W2004707103 · doi:10.1118/1.1644931

Quantification of bone strontium levels in humans by <i>in vivo</i> x-ray fluorescence

2004· article· en· W2004707103 on OpenAlexaff
Ana Pejović‐Milić, Ian Stronach, Janelle B. Gyorffy, Colin E. Webber, David R. Chettle

Bibliographic record

VenueMedical Physics · 2004
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsMcMaster UniversityHamilton Health SciencesMohawk CollegeToronto Metropolitan University
Fundersnot available
KeywordsStrontiumIn vivoNuclear medicineDetection limitExcited stateChemistryBiomedical engineeringMaterials scienceNuclear magnetic resonancePhysicsMedicineBiologyAtomic physicsChromatography

Abstract

fetched live from OpenAlex

The need for in vivo bone strontium assessment arises because strontium may exert a number of effects on bone, which may be either beneficial or toxic. Measurements discussed here are noninvasive, no sample is taken, nor is there discomfort to patients. The developed source excited x-ray fluorescence system employs a 109Cd source to excite the strontium K x rays, with the source and detector in approximately 90 degree geometry relative to the sample position. The factors affecting the accuracy and minimal detectable limit for bone strontium in vivo measurements are discussed. A system calibration revealed a minimum detectible limit of approximately 0.25 mg Sr/g Ca, which is sufficient for the monitoring of strontium levels in healthy subjects and patients with elevated bone strontium concentrations. Preliminary in vivo measurements in ten healthy subjects at two bone sites (phalanx and tibia) indicated that this system can be applied for cumulative bone strontium estimation while delivering a low effective dose of 80 nSv during the measurement time. Future work will involve attempts to enhance system precision with alternative fluorescing sources and further optimization of the detection system.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.538
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

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

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 head0.030
GPT teacher head0.320
Teacher spread0.290 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations47
Published2004
Admission routes1
Has abstractyes

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