Development of an In Vivo Bone Tungsten K‐X‐Ray Fluorescence Detection System
Bibliographic record
Abstract
ABSTRACT The versatility of tungsten (W) in electronics as well as in industrial and military applications has established it as one of the metals more present in our daily lives. Recent studies have highlighted its potential in the healthcare sector, including using sodium tungstate for anti‐diabetic treatment and W nanoparticles to enhance radiation therapy. With these expanding applications, W exposure to the public is increasing, necessitating the potential monitoring and investigation of W concentrations in humans. Tungsten exposure may lead to adverse health effects, such as pulmonary dysfunction, immune disorders, and cancer. In this study, we propose a design of an in vivo bone W K‐X‐ray Fluorescence diagnostic tool utilizing Monte Carlo modeling. Various excitation sources and detection geometries were explored to optimize the minimum detection limit and propose an effective diagnostic tool design. The modeling revealed that Cd or Co arranged in a 180° geometry exhibited a suitable detection limit and radiation dose for bone W quantification. However, the Cd excitation source arranged in a 180° geometry between the detector and bone is selected. Moreover, we investigated the normalization of W bone signals to enhance the clinical robustness of the diagnostic tool. Different normalization techniques were considered, including coherent, total spectrum, and Compton normalizations. It was demonstrated that Compton normalization outperformed coherent and total spectrum normalizations in correcting for the soft tissue thickness and different bone radii during the K‐XRF‐based quantification of W in human bone, with a variation from the mean value ≤ 10%.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".