Cortical porosity and the accuracy of 109Cd K XRF bone lead measurements in pre-menopausal women
Bibliographic record
Abstract
• 109 Cd K XRF bone Pb measurement accuracy is not affected by changes in tibia cortical porosity. • Monte Carlo modelling shows that in vivo heterogeneous distributions of bone Pb do not perturb XRF measurement accuracy. • Large tissue overlay thickness results in inaccurate 109 Cd K XRF tibia Pb measurements. • A non-linear endogenous exposure relationship between blood and bone Pb levels in young women has been observed Studies were performed to test whether tibia cortical porosity could create measurement artifacts in 109 Cd K XRF bone lead (Pb) measurements using Pb-doped plaster of Paris phantoms with a three-dimensional (3D) printed PLA lattice. Phantoms were modelled in MCNP to test the effect of cortical porosity; heterogeneous distributions of Pb; and extremely large tissue overlay thickness on bone Pb measurement accuracy. Modelling showed that cortical porosity of the tibia did not affect the measurement accuracy; measurements were inaccurate in phantoms with extreme heterogeneous distributions of Pb. However, it is only at unrealistic levels of heterogeneity (that are unlikely to be encountered in vivo ) that the measurement becomes inaccurate; and large tissue overlay thicknesses resulted in inaccurate 109 Cd K XRF bone Pb measurements. When blood and 109 Cd K XRF bone Pb data of morbidly obese women were excluded, age related changes in endogenous exposure to Pb were observed in pre-menopausal women.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".