A99 INCREASING THE NUMBER OF PASSES FOR ENDOSCOPIC ULTRASOUND-GUIDED FINE NEEDLE ASPIRATION BIOPSY OF SOLID MASS LESIONS – A QUALITY IMPROVEMENT INITIATIVE TO IMPROVE DIAGNOSTIC YIELD
Bibliographic record
Abstract
Abstract Background A retrospective chart audit (01/2022-12/2022) of endoscopic ultrasound (EUS)-guided fine needle aspiration biopsy (FNAB) of solid mass lesions revealed a disappointing diagnostic yield of 56% with a single needle pass. To improve this, a quality improvement (QI) intervention where endoscopists (three) were encouraged to perform three needle passes per patient was developed and trialed. Aims To assess intervention impact on improving the diagnostic yield of EUS-FNAB of solid mass lesions over a 9-month study period. Methods A chart audit was completed quarterly for all patients undergoing EUS-FNAB of solid mass lesions from 01/2024-09/2024. Descriptive statistics were completed. Only a definite diagnosis, as confirmed on histological examination, was considered when calculating the diagnostic yield. Results A total of 183 patients (112 M, 71 F), mean age 63±13 years (range 12-88 years), underwent 198 EUS-FNABs by 3 endoscopists over 9 months. A single pass with an FNAB needle was undertaken in 36/198 cases (18%). The diagnostic yield was 20/36 (56%), similar to the pre-intervention year. The solid mass lesions targeted were pancreas (16), lymph nodes (12), subepithelial (3), rectal and retro-peritoneal (2 each), and ampulla (1). One endoscopist performed 25/36 cases (69%) whereas the other two performed 11/36 (31%) and 0/36 cases. Proximity to vasculature and technical difficulty were reasons provided in 11 and 1 case(s), respectively, whereas no reason was documented in 24 cases. There was no difference in these variables between the groups with a definite diagnosis (20/36) vs. those without (16/36). After the first quarter audit, the need to avoid a single needle-pass was reinforced. Figure 1 shows the trend of single vs. 3 or more needle passes for the pre-intervention vs. the post-intervention year. There is a trend towards performing fewer single needle passes (39 pre vs. 36 post) and increasing 3 or more needle passes (25 pre vs. 68 post). This was associated with an improvement in diagnostic yield in the pre (22/39 [56%] vs. 21/25 [84%]) vs. post groups (20/36 [56%] vs. 61/68 [90%]). Conclusions Education, regular audit, and continued reinforcement has demonstrated an improvement in the diagnostic yield of EUS-FNAB by increasing the number of needle passes to 3 or more for solid mass lesions. Funding Agencies None
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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 | 0.010 | 0.025 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".