Coherent Scatter Computed Tomography for Structural and Compositional Stone Analysis: A Prospective Comparison with Infrared Spectroscopy
Bibliographic record
Abstract
INTRODUCTION: Infrared spectroscopy (IRS) is a standard method of stone analysis that yields relative proportions of stone materials within a sample. IRS is destructive, as it analyzes only powdered samples, with only a fraction of the stone being analyzed. This leads to sampling bias with components over- or underestimated or even missed entirely. IRS fails to provide structural composition such as that at the stone core. Coherent scatter computed tomography (CSCT) uses diagnostic X-rays to provide detailed structural and compositional analysis of intact specimens, including detailed imaging of the stone core. METHODS: Consecutive patients undergoing surgical treatment for stone disease were recruited for the study. Stones or fragments collected during surgery were subjected to both CSCT and IRS. The two methods were compared with respect to overall bulk composition of the stone and the ability to identify the material at the core. RESULTS: CSCT and IRS agreed on the primary component in the majority (84.8%) of samples. CSCT detected additional components in 88.8% of stones identified as uniform by IRS. CSCT also identified a distinct stone core in 78.8% of samples, while IRS failed to detect the core component in 21.2% of these stones. In 30.3% of the stones with a core component, IRS did not identify the core mineral as the primary component. CONCLUSION: CSCT provides superior quantitative stone analysis and is not prone to issues such as sampling error as the entire specimen is analyzed. CSCT offers excellent structural imaging of stone samples, including detailed analysis of core composition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".