Feasibility of a <sup>109</sup>Cd-based portable XRF device for measuring skin iron concentration in anaemic and β−Thalassaemic patients
Bibliographic record
Abstract
Abstract Iron is an essential element vital for growth and development. The severe effects on the body due to iron deficiency or overload have prompted sustained research into accurate in vivo iron measurement techniques for the past several decades. X-ray fluorescence (XRF) analysis of iron in the body has been investigated in this work because of the non-invasive nature of the technique. A system has been designed using a silicon drift detector to measure the low-energy iron K α x-rays excited in the samples by the silver x-rays from 109Cd of energy 22 keV and 25 keV. The source is contained within a tantalum shielding cap designed to reduce the spectral background. The system was calibrated against 3D printed polylactic acid (PLA) phantoms filled with solutions of iron at various concentrations. The iron x-ray signals were normalized to a nickel x-ray signal which improved the system’s reproducibility. The 3D phantoms and normalisation resulted in a linear calibration line (p < 0.001 and r2 > 0.999). For a real-time measurement of 1800 s, the minimum detectable limit for the system was measured to be 1.35 ± 0.35 ppm which is achieved with a low radiation dose of 1.1 mSv to the skin surface. This low detection limit and low dose mean the system is feasible for application to human measurements in both iron deficiency and overload disease. The system will proceed to post-mortem validation studies prior to in vivo system efficacy testing.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".