The Validity of Ultrasound KUB; in the Diagnosis of Ureteric Calculus
Bibliographic record
Abstract
Background: Diagnostic techniques have utilized to diagnose ureteric calculus in patients presenting with ureteric calculus. The availability, cost and expertise of these techniques varies greatly from region to region. Among them ultrasound is the widely available, can be rapidly performed and also can be repeated without any exposure to radiation. We conduct this study which helps to establish the level of sensitivity, the specificity level, the value of Positive Prediction and the value of the negative prediction KUB ultrasound in the diagnosis of ureteric calculus. Methods: They collect this data from 193 patients from suspected ureteric calculus. All these 193 patients underwent for Ultrasound KUB followed by non-contrast CT KUB. These results of ultrasound are compared with CT KUB, which helps to report and to determine the accuracy level of ultrasound. Results: The USG enables and helps diagnose 105 number of cases in 193 patients with the accuracy of 62.60%. Number of cases diagnosed as having ureteric calculus on CT KUB, were 171 out of total 193 patients with 22 cases being missed. Conclusions: This ultrasound is a key to the preliminary test for the diagnosis of ureteric calculus. These results show some significant relationship for the last and final CT KUB report. The Professional level of experts used to perform this task.
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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 | 0.007 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".