Clinical Application of CT Imaging Grading System in Upper Urinary Tract Calculi with Kidney Infection
Bibliographic record
Abstract
PURPOSE: This study aimed to establish a CT imaging grading system and explore its value in evaluating upper urinary tract calculi associated with kidney infections. METHODS: CT images of 126 patients with kidney infections caused by upper urinary tract calculi were retrospectively analyzed. The CT grading system was developed based on CT images. CT images were classified into 4 grades. General information, symptoms, and clinical findings of patients in different CT grades were analyzed. With the occurrence of systemic inflammatory response syndrome (SIRS) as the endpoint, univariate and multivariate analysis was conducted to analyze the risk factors of SIRS. RESULTS: < 0.05): the white blood cell count, urine leucocytes count, CT1, CT2, maximum body temperature, duration of disease, the proportion of blood neutrophils, the size of stones, and levels of the C-reactive protein and procalcitonin. Only CT grading was statistically significant after multivariate analysis. According to the values of the partial regression coefficient (B), the higher the CT grade, the greater the risk of SIRS. The risk of SIRS was 4.472 times higher with each increment of the CT grade. CONCLUSIONS: The CT grade is directly associated with clinical symptoms and the risk of SIRS.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".