Factors influencing uncertainties of in vivo bone lead measurement using a<sup>109</sup>Cd K X-ray fluorescence clover leaf geometry detector system
Bibliographic record
Abstract
A (109)Cd K X-ray fluorescence (KXRF) measurement system consisting of four detectors in clover-leaf geometry is a non-invasive, low-radiation-dose method of measuring bone lead concentration. Its high precision in estimating the bone lead content makes it a promising tool for the determination of the low levels of lead currently found in the general population. After developing the clover-leaf geometry system, the system was used for the first time in a major survey in 2008 to measure the lead levels of 497 smelter employees (an occupationally exposed group with high lead levels). Since the delivered effective dose of the bone lead system in clover-leaf geometry is small (on the order of nSv), the technique can be used to measure the bone lead of sensitive populations such as the elderly and children. This detector system was used from 2009 to 2011, in a pilot study that measured the bone lead concentration of 263 environmentally exposed individuals (termed the EG group) residing in Toronto, Ontario, Canada. In this paper, the factors that influence uncertainties in lead content in tibia (cortical bone) and calcaneus (trabecular bone) are discussed based on gender, age, and body mass index (BMI) by using analysis of variance (ANOVA) and multiple linear regression models. Results from the two study groups (the EG group versus the occupationally exposed smelter employees) are compared where appropriate (i.e. for males older than 20). Results from univariate analyses showed that females have higher tibia uncertainty compared to males. We observed significant differences for both calcaneus and tibia uncertainty measures (p < 0.0005) among different age groups, where the uncertainties were highest in the lowest age group (<11 years). Lastly, and perhaps most significantly, we found that the product of source activity and measurement time influenced the precision of measurements greatly, and that this factor alone could account for the higher uncertainties observed for the male cohort of the EG group versus the smelter employees.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".