DIAGNOSTIC ADEQUACY AND SAFETY OF IMAGE GUIDED TRU-CUT BIOPSY
Bibliographic record
Abstract
Objective: To analyze the safety and adequacy of image guided TRU-CUT biopsy in Kuwait Teaching Hospital, Peshawar
 Materials and Methods: This retrospective cross-sectional study was conducted in Radiology Department of Kuwait Teaching Hospital from 1st January to 31st December 2016. A total 354 patients presenting for image guided TRUCUT biopsies were included in study, specimens were sent to reputable laboratories for evaluation of sample adequacy whereas, safety of the procedure was assessed by rate of major complications. SPSS version19 was used for statistical analysis.
 Results: 100% of CT guided biopsies generated adequate samples, whereas 326 out of 336 U/S guided biopsies produced adequate specimen with overall diagnostic adequacy of 97.1%. Scrutiny of results depicts no major complications in any patient. There was statistically insignificant effect of needle parameters or imaging modality, having P value > 0.005, on the adequacy of biopsy specimen.
 Conclusion: Image guided TRU-CUT biopsy is effective and safe procedure. Our study can help counsel patients about safety and effectiveness of procedure and avoiding more invasive open biopsies.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".