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Record W2074908350 · doi:10.1088/0031-9155/51/2/011

<i>In vivo</i>investigation of a new<sup>109</sup>Cd γ-ray induced K-XRF bone lead measurement system

2006· article· en· W2074908350 on OpenAlexaff
Huiling Nie, David R. Chettle, Luo Li, J. M. O’Meara

Bibliographic record

VenuePhysics in Medicine and Biology · 2006
Typearticle
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsUniversity of GuelphMcMaster University
Fundersnot available
KeywordsMonte Carlo methodTibiaLead (geology)Nuclear medicineImaging phantomDosimetryMaterials scienceBiomedical engineeringMedicineMathematicsStatisticsSurgeryBiology

Abstract

fetched live from OpenAlex

A new 109Cd gamma-ray induced K-XRF bone lead measurement system using an array of four detectors has been developed. Previous results from Monte Carlo (MC) simulations and experiments with phantoms predicted that it would be about three times more sensitive than the conventional system, albeit using a more active source. A dosimetry study has been performed for this system and it demonstrated that the dose delivered to the measured individuals is acceptable even for 5-year-old children. Approval to apply this system to human studies has been received from the Research Ethics Board. In this study, 20 adult volunteers, 10 male, 10 female, were recruited to have their tibia measured with both the conventional system and the new system. The result confirmed the improvement predicted by the MC simulations and the in vitro measurements. Two other interesting points were discovered from the data. One is that the data from the new system showed a significant positive correlation between age and tibia lead concentration, while the data from the conventional system do not. The other is that 85% of the tibia lead concentrations were under the minimum detection limit when measured by the conventional system, and the proportion reduced to 50% when measured by the new 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)0.001
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: Empirical
Teacher disagreement score0.358
Threshold uncertainty score0.454

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.156
GPT teacher head0.335
Teacher spread0.178 · 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

Citations53
Published2006
Admission routes1
Has abstractyes

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