SU-GG-I-152: Structure and Composition Kidney-Stone Analyses Using Coherent-Scatter Computed Tomography: Early Results with a Novel Laboratory Method
Bibliographic record
Abstract
Purpose: Many kidney stones have a heterogeneous composition in layers about a central core. At present, infrared spectroscopy (IRS) or conventional x-ray diffractometry are used to determine stone composition which is required to determine appropriate follow-up care for recurrence prevention. Both methods are limited requiring that stones be crushed prior to examination and a bulk measure of composition is obtained. We are currently conducting a clinical trial using coherent-scatter computed tomography (CSCT) as an ex-situ technique of stone analysis. We believe this technique will provide detailed structural information, including the initial nucleating mineral at the core of the stone. Method and Materials: Our ongoing prospective trial examines urinary calculi from 100 consecutive eligible patients undergoing either percutaneous nephrolithotripsy or ureteroscopy. All calculi removed intra-operatively are analyzed with CSCT and then the same samples are sent for IRS (the standard of care at our center). Stone analyses from the two modalities are compared for overall bulk composition and the CSCT data analyzed to determine composition of the core. Results: Of the first 25 stones examined, 12 had core minerals that were not the primary component reported by IRS. In four, IRS reported 100% cystine where CSCT showed a distinct UA core inside cystine. In another, IRS reported 85% struvite while CSCT showed a calcium oxalate monohydrate core inside a struvite outer layer. Conclusion: We are finding a significant fraction of stones in which IRS does not report the mineral component at the core of the stone as a large component of bulk composition. This raises the question whether recurrence rates could be improved by targeting the core component.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".