Efficacy, Feasibility, and Safety of Endoscopic Ultrasound-guided Fine-needle Biopsy for the Diagnosis of Gastrointestinal Subepithelial Lesions
Bibliographic record
Abstract
BACKGROUND: Endoscopic ultrasound (EUS) fine-needle biopsy (FNB) has become an efficient method for diagnosing gastrointestinal (GI) subepithelial lesions (SELs). However, recent guidelines have not regarded FNB as the primary strategy for diagnosing GI SELs. We performed this study to systematically measure the efficacy, feasibility, and safety of EUS-FNB in diagnosing GI SELs. MATERIALS AND METHODS: Relevant studies were searched in PubMed and EMBASE and published after January 2015 were included. The overall rates of diagnostic yield, technical success, and adverse events were calculated as outcome measures. The Jadad scale and the Newcastle-Ottawa scale were used to evaluate the quality of the trials, funnel plots and Egger's test were used to measure publication bias, and sensitivity and subgroup analyses were performed to explore the variance of heterogeneity and sensitivity, respectively. RESULTS: Sixteen studies analyzing 969 patients between 2015 and 2020 were included. Studies showed little change in sensitivity, and 13 were considered high quality. A certain degree of publication bias existed in the diagnostic accuracy rate. The overall rates of diagnostic yield, technical success, and adverse events were [85.69% (95% confidence interval (CI): 82.73-88.22, I2=41.8%), 98.83% (95% CI: 96.73-99.97, I2=54.3%), and 1.26% (95% CI: 0.35-2.54, I2=0.0%)]. No clinical influencing factors were identified in the subgroup analysis. CONCLUSIONS: EUS-FNB is a promising technology with a relatively superior diagnostic yield, technical success, and security, which is an optimal option for the diagnosis of SELs.
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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 | 0.041 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.011 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".