Evaluation of the bias on X-ray Absorptiometry and Quantitative Ultrasound Measurements due to Bone-Seeking Elements
Bibliographic record
Abstract
The current clinical gold standard for assessing bone mineral density (BMD) is by dual-energy X-ray absorptiometry (DXA), which estimates areal BMD (aBMD). The substitution of bone-seeking elements such as strontium, lead and aluminum in bone mineral alters the photon attenuation properties of bone, and therefore can cause biases in aBMD when estimated by DXA. Quantitative ultrasound (QUS) is an alternative bone densitometry technique based on the measurement of broadband ultrasound attenuation (BUA) and speed of sound (SOS). Derived quantity known as the stiffness index (SI) is calculated based on the two ultrasonic parameters, and is then converted to an estimated aBMD. This study aimed to evaluate the effect of strontium, lead and aluminum on DXA and QUS measurements using bone-mimicking phantoms. To prepare the phantoms, hydroxyapatite compounds that contain varying concentrations of the three elements were set in gelatin as to have a uniform volumetric BMD (vBMD) of 200 mg/cm 3 . A Hologic Horizon ® DXA device and a Hologic Sahara ® QUS device were used to estimate aBMDs in the phantoms. aBMD measured by DXA exhibited a strong linear relationship ( r = 0.995, p 0.05) was found between aBMD and concentrations of lead or aluminum under 200 ppm examined in this study. In the case of QUS, BUA was found to vary linearly with lead concentration ( r = 0.899, p < 0.038), but it showed no statistically significant association with strontium and aluminum. Furthermore, no statistically significant changes were observed for SOS and SI, as a function of the concentration of the three elements. This result suggests that, unlike DXA, the QUS is capable of assessing bone density independently of strontium substitution. Furthermore, clinically relevant concentration of lead and aluminum does not seem to influence DXA and QUS measurements.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".