Unenhanced computed tomography findings of renal papillae in patients with a ureteral stone
Bibliographic record
Abstract
PURPOSE: In some patients with a ureteral stone without uretero-hydronephrosis, it is difficult to determine the location of the stone. The objective of the present study was to investigate the changes in renal papillae using unenhanced computerized tomography (uCT) and determine the side of calculi using the renal papillary findings in patients with a ureteral stone. METHODS: uCT data from 81 patients were retrospectively reviewed for this study. The inclusion criteria were unilateral ureteral calculi, no renal calculi and no hydronephrosis. For each patient, three measurements of CT attenuation of 0.05 cm2 area were made in the tip of the interested renal papillae, both stone side and non-stone side. Student's t test was used for statistical analysis. RESULTS: Forty-one right-sided and 40 left- sided isolated unilateral ureteral calculi patients were evaluated by uCT exam. The average attenuations of the tip of the papillae in stone side and non-stone side were 34.1 Hounsfield units (HU) and 30.6 HU, respectively. There was a statistically significant difference between stone and non-stone sides (p< 0.05). CONCLUSION: During routine practical uCT applications, it can be difficult to distinguish phleboliths, ureteral stone or the existence of non-opaque ureteral stone, so papillae density measurements can be a practical method to identify the existence of ureter stone and its location (side).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".