Investigating The Coherent Normalization Method For A Bone Gadolinium X-ray Fluorescence Measurement System: A Monte Carlo Study
Bibliographic record
Abstract
Elevated gadolinium levels in patients with healthy renal function exposed to gadolinium based contrast agents (GBCA) have been confirmed by studies in the literature. Though the potential long-term effects of the retained gadolinium are still unknown, symptoms such as bone and joint pain have been reported in some subjects. As detecting the presence of gadolinium in vivo is required to diagnose toxicity related medical conditions, a 109Cd based X-ray fluorescence (XRF) bone gadolinium measurement system has been previously developed. The current method is dependent on geometrical factors and other interpatient factors, such as the size and shape of the bone and the tissue thickness overlying the measurement site, which reduces the robustness of the measurement system and causes the need for correction factors. It was previously shown that the successful use of the coherent normalization procedure can eliminate the need for such corrections and is conditional on four criteria that must be met. When detecting gadolinium two of the four criteria are not satisfied which makes further investigation of the method required. This work investigates the feasibility of the coherent normalization method to correct for the effect of varying overlying tissue thickness of an adult population through Monte Carlo simulations. The coherent normalization method was studied as a function of overlying tissue thickness (OTT) to represent varying body types. The average coherent ratio (Gd K X-ray counts/Coherent counts) was found to be 0.717 ± 0.025 and the normalization resulted in a line with a slope of -0.0053± 0.0040 which is insignificant at the 95% confidence level (p = 0.43) suggesting the validity of the method. Additionally, this thesis provides a theoretical explanation of the feasibility of the method regardless of not fulfilling all four criteria, through introducing the Secondary Fluence Fluorescence Factor (SFFF) and separating fluence components, primary and secondary, contributing to the fluorescence of gadolinium.
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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 | 0.002 | 0.006 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".