Evaluation of imaging technologies to correct for photon attenuation in the overlying tissue for <i>in vivo</i> bone strontium measurements
Bibliographic record
Abstract
The interpretation of measurements of bone strontium in vivo using energy dispersive x-ray fluorescence spectroscopy is presently hindered by overlying skin and soft-tissue absorption of the strontium x-rays. The use of imaging technologies to measure the overlying soft-tissue thickness at the index finger measuring site might allow correction of the strontium reading to estimate its concentration in bone. An examination of magnetic resonance (MR), computed tomography (CT) and high-frequency ultrasound (US) imaging technologies revealed that 55 MHz US had the smallest range of measurement uncertainty at 3.2% followed by 1 Tesla MR, 25 MHz US, 8 MHz US and CT at 4.3, 5.4, 6.6 and 7.1% uncertainty, respectively. Of these, only CT imaging appeared to underestimate total thickness (p < 0.05). Furthermore, an inter-study comparison on the accuracy of US measurements of the overlying tissue thickness at finger and ankle in nine subjects was investigated. The 8 MHz US system used in prior in vivo experiments was found to perform satisfactorily in a repeat study of ankle measurements, but indicated that finger thickness measurements may have been misread in previous studies by up to 17.7% (p < 0.025). Repeat ankle measurements were not significantly different from initial measurements at 2.2% difference.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".