XRF analysis of strontium: Exploring cellulose as a soft tissue equivalent
Bibliographic record
Abstract
X‐ray fluorescence (XRF) is a widely used method for in vivo elemental analysis. Particularly for bone, it is a non‐invasive technique that provides information on composition without significant risk to the patient. XRF contributes a capability for measuring elements beneficial to human health, such as strontium. This is a proposed supplement that has been shown in clinical trials to reduce fracture risk in people diagnosed with osteoporosis. Although XRF is a viable method for quantifying bone strontium, there are still factors that constrain its effectiveness. X‐ray attenuation through overlying soft tissue decreases the signal, consequently requiring correction before estimating the true concentration of strontium in bone. A correction factor can be applied to account for the reduced signal, but an accurate measurement of overlying soft tissue thickness is required. It has been shown that using the correlation between Compton peak count rate and overlying thickness can be used as an estimation of overlying tissue. Lucite is commonly used as a soft tissue substitute; however, its mean atomic number is appreciably lower than soft tissue, somewhat limiting its applicability. This study tests the feasibility of using cellulose filter papers as a substitute for overlying soft tissue to perform XRF analysis of strontium‐doped hydroxyapatite bone phantoms. Mass attenuation coefficients are shown to be closer to those of soft tissue (International Commission on Radiation Units' four‐component) than Lucite, and the Compton correlation is used to estimate thickness as a correction factor to quantify true strontium concentration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".