The role of rectal endoscopic ultrasonography plus fine needle aspirartion and fine needle biopsy in pelvic masses
Bibliographic record
Abstract
BACKGROUND AND AIM: The diagnostic role of endoscopic ultrasound (EUS) guided fine needle aspiration (FNA) and fine needle biopsy (FNB) of pelvic masses has not been well established. We aimed to evaluate the diagnostic accuracy of EUS plus FNA/FNB in suspected local recurrence of pelvic masses. MATERIALS AND METHODS: All consecutive patients with a history of lower gastrointestinal and pelvic mass undergone EUS-FNA/FNB were included in the study. RESULTS: In total 34 patients who underwent EUS-guided FNA or FNB of a perirectal mass were enrolled. The sampled lesion was a mass in 22 patients (64.7%) and a lymph node in 10 patients (29.4%). The univariate logistic regression analysis for diagnostic accuracy showed lesion size as a significant predictor of diagnostic accuracy [odds ratio (OR), 1.61; 1.08-2.27; P = 0.02]. Diagnostic sensitivity was 100% (71.5-100%) with EUS-FNB and 75% (34.9-96.8%) with EUS-FNA ( P = 0.12); specificity was 100% in both groups ( P = 1.0). Sample adequacy was 94.1% in the whole cohort, with 20/20 adequacy rate (100%) in the EUS-FNB group and 12/14 (85.7%) in the EUS-FNA group ( P = 0.28). CONCLUSION: This is the first study demonstrating the diagnostic yield of EUS plus FNA/FNB in patients with pelvic masses comparing the two needles. Our results highlight the relevance of this technique, especially in undefined masses during oncological follow-up.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".