The Efficiency of Non-Contrast Computed Tomography in the Estimation of Urinary Stone Composition
Bibliographic record
Abstract
Background : Prior knowledge of stone composition is key to determining stone brittleness as well as treatment management and prophylactic approach. The present study seeks to visualize stone type on non-contrast computed tomography on the basis of Hounsfield Unit (HU) values. Methods : A retrospective evaluation was performed of non-contrast computed tomography scans of patients who underwent urinary system operation to remove stones which were subjected to biochemical analysis. The localization and size of the stones were determined and their HU values, mean attenuation/size (HUD) and maximum attenuation/size ratios were calculated. Results : The results of stone analysis revealed 34 calcium phosphate, 11 calcium oxalate, 5 triple phosphate (struvite) stones. On the basis of measurement results, a significant difference was identified among HU values of the three stone types (P = 0.002). When the stones were compared in pairs, this difference was established to be due to the difference between the densities of calcium phosphate and struvite stones. No significant difference was observed among the stone groups with regard to HUD and maximum attenuation/size ratios. Conclusions : HU values are a useful parameter to distinguish between calcium phosphate and struvite stones. The inclusion of HU values in reports will set the right course for treatment. doi:10.4021/wjnu4e
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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 | 0.001 |
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".