Analysis of urinary stone components by x-ray coherent scatter: characterizing composition beyond laboratory x-ray diffractometry
Bibliographic record
Abstract
Monoenergetic x-ray diffraction (XRD) analysis is an established standard for the assessment of urinary stone composition. The inherent low energy of x-rays used (8 keV), however, restricts penetration depth and imposes a requirement for small powdered samples. A technique capable of producing detailed information regarding component structural arrangements in calculi non-destructively would provide clearer insights into causes of formation and subsequent growth and allow the selection of more appropriate courses of therapy. We describe a new method based on the detection of coherent scatter (CS) in stone components using polyenergetic x-rays (70 kVp) from diagnostic equipment. While the higher energy allows the analysis of intact calculi, the polyenergetic source causes an angular broadening of measured CS patterns. We show that it is possible to relate the polyenergetic (CS) and monoenergetic (XRD) measurements through a superposition integral of the monoenergetic XRD cross-section with a function representative of the polyenergetic spectrum used in CS. Experimentally acquired diffractometry cross-sections of the seven major urinary stone components were subjected to this operation, revealing good agreement of diffraction features with CS. Therefore, our CS analysis is sensitive to stone component structure, similar to conventional XRD analysis. This indicates that CS analysis can be used as a basis to classify urinary calculi by composition. The potential of identifying stone components non-destructively was demonstrated from a tomographic CS analysis of a stone-mimicking phantom. Tomographic composition maps were generated from CS patterns, showing the structural arrangement of multiple stone components within the phantom. CS analysis has the ability to detect components in the presence of many others. The ability to perform CS measurements in intact calculi would allow for the identification of stone structures critical to patient metaprophylaxis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".