Coherent normalization of finger strontium XRF measurements: feasibility and limitations
Bibliographic record
Abstract
A non-invasive in vivo x-ray fluorescence (XRF) method of measuring bone strontium concentrations has previously been reported as a potential diagnostic tool able to detect strontium concentration in the finger and ankle bones. The feasibility of coherent normalization for (125)I-source-based finger bone strontium x-ray fluorescence (XRF) measurements is assessed here by theoretical considerations and Monte Carlo simulations. Normalization would have several advantages, among which are the correction for the signal attenuation by the overlying soft tissue, and intersubject variability in the bone size and shape. The coherent normalization of bone strontium XRF measurements presents several challenges dictated by the behaviour of the coherent cross section and mass attenuation coefficient at the energies involved. It was found that the coherent normalization alone with either 22.1 keV or 35.5 keV photons was not successful in correcting for the overlying soft tissue attenuation. However, it was found that the coherent peak at 35.5 keV was able to correct effectively for variability in the finger bone size between people. Thus, it is suggested that, if the overlying soft tissue thickness can be obtained by means of an independent measurement, the 35.5 keV peak can be used to correct for the bone size, with an overall accuracy of the normalization process of better than 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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".