Assess the Benefit of Interventional Radiologist Performing an Image Guided Biopsy
Bibliographic record
Abstract
Abstract Purpose To determine whether there is any benefit in diagnostic accuracy, reduction in periprocedural complications, when image-guided biopsies are performed by interventional radiologist as compared to a general radiologist or other physician without prior special interventional training. Patients and Methods This study is a retrospective chart review of all consecutive patients that underwent imaging-guided core biopsies during one year in our hospital. Information collected included: patient age, gender, coagulation status, organ biopsied, imaging modality and equipment used, number of samples obtained, post-biopsy complications, histopathological results, and previous interventional training of a physician performing the biopsy. The quantitative data from patient charts was analyzed using a statistical program, Statistical Package for Social Sciences (SPSS). Results 449 patients were included in this study: 132 were performed using Computed Tomography guidance and 317 were performed under ultrasound guidance. The success rate of core biopsies was compared between different specialists, and was measured based on periprocedural complications, necessity of medical intervention and/or hospitalization after biopsy procedure, true positive histopathology results, and need to perform repeat biopsy of the same lesion owing to inconclusive results. Overall, IR had a success rate of 88.13%, non-IR had a success rate of 61.07%, and nephrologists had a success rate of 77.65%. The post biopsy complication rates were 5.48%, 27.52%, and 17.65% for procedures performed by IR, non-IR, and nephrologists respectively. Conclusion Our study shows an overall higher success rate and improved patient outcome when image guided biopsies were performed by IR, as compared to other non-IR physicians.
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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 | 0.002 | 0.015 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".