Predictability of Computerized Tomography as Compared to Ureteroscopy in Detection of Ureteric Stone in Patients with Indwelling Stents
Bibliographic record
Abstract
Objective: The current gold standard for the diagnosis of ureteric stones in patients with a stent in situ is ureteroscopy but this study is planned to determine the positive predictive value of computerized tomography (CT) scan among such patients. Methods: This study involved patients who had ureteral stent in situ and were referred for re-evaluation of residual stones after extracorporeal shock wave lithotripsy. These patients underwent CT-scan for detection of ureteric stone. Later on, ureteroscopy was performed and ureteric stone was confirmed on direct visualization. Results: The mean age of the patients was 32.2±8.9 years. Male to female ratio of 1.7:1. CT scan shows a stone in 252 patients (70.2%) out of which 165 (46.0%) were confirmed on ureteroscopy. This yielded a sensitivity of 88.7 %, specificity of 49.7 %, positive predictive value of 65.5%, negative predictive value of 80.4% and diagnostic accuracy of 69.9% of CT for detecting ureteric calculi in patients with ureteric stents (p value < 0.0001). Conclusion: CT scan owing to its limited diagnostic accuracy cannot replace ureteroscopy for detection of ureteric stones in patients with ureteric stents.
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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 | 0.001 | 0.016 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".