Ultrasound-guided renal biopsy: experience using an automated core biopsy system.
Bibliographic record
Abstract
OBJECTIVE: To assess the safety and efficacy of ultrasound-guided percutaneous renal biopsy using an automated core biopsy system, and to determine radiologists' accuracy in predicting sample adequacy. METHODS: Ninety-five biopsies were performed on 25 native kidneys and 70 renal allografts using a 16-gauge automated, spring-loaded core biopsy device under real-time sonographic guidance. Radiologists performing the biopsy estimated the number of core samples needed to obtain an adequate specimen, based on visual inspection of each core. The final determination of the number of samples was made by a pathology technologist who attended each biopsy, based on preliminary microscopic examination of tissue cores. After each biopsy, an ultrasonographic examination was performed to search for biopsy-related hemorrhage, and a questionnaire was given to the patient to determine biopsy-related complications, which were categorized as either minor or major. RESULTS: The main indication for biopsy was acute renal failure (in 43.2% of biopsies). An average of 3 tissue cores per biopsy were obtained. Of the 94 patients in whom a biopsy was conducted to exclude diffuse renal disease, a mean of 12.5 glomeruli were present in each specimen. Overall, adequate tissue for diagnosis was obtained in 98.9% of cases. The radiologists' estimate of the number of core samples needed concurred with the pathology technologists' determination of sample adequacy in 88.4% of cases. A total of 26 complications occurred (in 27.4% of biopsies), consisting of 23 minor (24.2%) and 3 major (3.2%) complications. CONCLUSIONS: Real-time sonographic guidance in conjunction with an automated core biopsy system is a safe and accurate method of performing percutaneous renal biopsy. Routine use of sonographic examinations to search for biopsy-related complications is not indicated. Radiologists are accurate in estimating sample adequacy in most cases; however, the presence of a pathology technologist at the biopsy procedure virtually eliminates the possibility of obtaining insufficient tissue for histologic 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 | 0.005 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".