Application and methodology of <i>in vivo</i> K x‐ray fluorescence of Pb in bone (impact of KXRF data in the epidemiology of lead toxicity, and consistency of the data generated by updated systems)
Bibliographic record
Abstract
Abstract K x‐ray fluorescence (KXRF) technology has been used to make in vivo measurements of lead in bone for more than three decades. The data obtained are beneficial to research on lead toxicity as well as, in certain circumstances, the practice of occupational and environmental medicine. This paper reviews the impact of KXRF data on epidemiologic research involving lead toxicity and demonstrates that bone lead is and will continue to be a valuable biomarker in addressing long‐term health effects related to cumulative exposure. The KXRF system has been improved and upgraded several times ever since it was first used. The consistency of the data obtained from these KXRF systems has been investigated in many studies. This paper provides an overview of the factors that will affect the data generated by the KXRF systems. A calibration problem encountered in one of the major KXRF laboratories is described, and the approach taken to solve the problem is discussed. Despite all the theoretical considerations, there are still some important practical challenges to the intercalibration of KXRF instruments both within the laboratory, and between laboratories. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".