Coherent normalization for<i>in vivo</i>measurements of gadolinium in bone
Bibliographic record
Abstract
OBJECTIVE: Recent evidence of gadolinium (Gd) deposition in bones of healthy individuals who have previously received Gd-based contrast agents (GBCAs) for MRI has led to a demand for in vivo measurement techniques. The technique of x-ray fluorescence provides a low risk and painless method to assess Gd deposition in bone, and has the potential to be a useful clinical tool. However, interpatient variability creates a challenge while performing in vivo measurements. APPROACH: We explored the use of coherent normalization, which involves normalizing the Gd K x-rays to the coherent scattered γ-ray from the excitation source, for bone Gd measurements through a series of phantom-based experiments and Monte Carlo simulations. MAIN RESULTS: We found coherent normalization is able to correct for variation in overlying tissue thickness over a wide range (0-12.2 mm). The Gd signal to coherent signal ratio is independent of tissue thickness for both experiments and Monte Carlo simulations. SIGNIFICANCE: Coherent normalization has been demonstrated to be used in practice with normal healthy adults to improve in vivo bone Gd 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".