Prospective Evaluation of Doppler Sonography to Detect the Twinkling Artifact Versus Unenhanced Computed Tomography for Identifying Urinary Tract Calculi
Bibliographic record
Abstract
OBJECTIVES: The twinkling artifact is an emerging tool for identifying urinary tract calculi. The purpose of this prospective study was to evaluate the diagnostic accuracy of the twinkling artifact compared to unenhanced computed tomography in detecting urolithasis. METHODS: After Research Ethics Board approval, 51 patients with flank pain from the emergency department were enrolled between November 2009 and September 2010. Patients received an unenhanced computed tomographic scan with 1.25-mm raw data and reformatted 5-mm axial and 2-mm coronal images. Blinded assessment of the urinary tract was performed with gray-scale and color Doppler interrogation. The number of calculi, location, size, kidney distance from the skin, body mass index of the patient, and sonographic image parameters were recorded. RESULTS: There were 35 right-sided and 38 left-sided renal calculi, 14 right-sided and 21 left-sided ureteric calculi, and 6 bladder calculi (total, 114 calculi). Thirteen patients had no calculi. The average calculus size was 2.6 mm (range, 1-9 mm). There were 6 false-positive and 22 false-negative instances of twinkling artifacts. On gray-scale evaluation looking for an echogenic focus with shadowing, there were 8 false-positive and 40 false-negative findings. The positive predictive value (PPV) of the twinkling artifact for identifying calculi was 94%, and the sensitivity was 83%. The PPV of gray-scale sonographic shadowing was only 64.9%, and the sensitivity was 80.2%. CONCLUSIONS: The twinkling artifact has a high PPV for detecting renal and urinary tract calculi. Evaluation for the twinkling artifact is a complementary technique to standard gray-scale shadowing of calculi and improves detection of urolithiasis on sonography.
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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 | 0.004 | 0.018 |
| 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 | 0.000 |
| 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 | 0.000 |
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".