Comparison of DMSA scan 99 m and EC scan 99 m in diagnosis of cortical defect and differential renal function
Bibliographic record
Abstract
INTRODUCTION: Diagnosis of renal cortical lesions by radioisotopes in nuclear medicine is one of the most common techniques and procedures can be performed by different radiotracer. However, all these materials are accurate in determining kidney function, but there are differences between them in the field. The purpose of this study was to evaluate the effectiveness of EC scans compared with DMSA scan in the detection of cortical lesions and DRF. METHODS: 65 cases, which have been referred for various reasons, for DMSA scans were enrolled. Patients 1 week after DMSA scan with the previous consent of the EC being scanned. The results were compared in terms of convergence as well as sensitivity, specificity, positive and negative predictive value of EC with respect to the results of DMSA scan. RESULTS: PPV of EC was 100%, negative predictive value of EC was 68.75%, sensitivity of EC was 90.74% and specificity of EC was 100% in the detection of cortical lesions. DMSA scan and EC convergence rates result in cortical lesions in our study was high. DISCUSSION: We suggest EC scan as an alternative to reduce the cost of therapy and radiation, but considering the benefits of DMSA scan, it could remain the gold standard method of diagnosis.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".