A90 ASSESSMENT OF A MULTI-COMPONENT QUALITY IMPROVEMENT INTERVENTION TO IMPROVE DIAGNOSTIC YIELD FROM ENDOSCOPIC ULTRASOUND-GUIDED FINE NEEDLE ASPIRATION BIOPSY OF SOLID MASS LESIONS
Bibliographic record
Abstract
Abstract Background A chart audit to review endoscopic ultrasound (EUS)-fine needle aspiration biopsy (FNAB) of solid mass lesions from 01/2022-12/2022 identified a diagnostic yield of 75%. To improve this, a quality improvement intervention including increasing the number of needle passes to 3/case, improving needle pass documentation in endoscopy reports, reducing the number of individuals making cytology slides, and using formalin as transport medium for cell block preparation instead of saline was developed and trialed for 9 months. Aims To assess intervention impact on improving the diagnostic yield of EUS-FNAB of solid mass lesions. Methods Three endoscopists were provided targeted education and a chart audit was completed 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 (who made cytology slides, ensured transport medium and completed documentation). Pancreatic masses were the most common (122/198, 62%). A 22-gauge FNAB needle was used in 194/198 (98%) cases. A total of 420 needle passes were performed for 191 patient cases (mean 2.2/case) compared with mean of 1.9/case pre-intervention. Documentation improved, 7 cases (3.5%) did not have the number of needle passes specified compared with 51 cases (26%) pre-intervention. Tissue samples were transported in formalin for histology in 197 cases, on cytology slides in 165 cases, and in formalin for cell block preparation in 42 cases, similar to pre-intervention. Overall, a definite diagnosis was achieved in 156/198 cases (79%) compared with 149/200 (75%) in the pre-intervention year. Stratifying for needle passes, a definite diagnosis was achieved in 20/36 (56%), 69/87 (79%), 56/63 (89%), and 5/5 (100%) cases that had 1, 2, 3, and >3 needle passes, respectively. The number of passes was seen to independently impact diagnostic yield regardless of type of solid mass, endoscopist, type/size of needle used, and whether or not tissue was provided for cell block preparation. Conclusions Although we did not reach our goal of 3 needle passes per case, documentation and the number of tissue samples transported in formalin improved. The results suggest 3 or more needle passes per case may improve diagnostic yield and attempts should be made to avoid performing single needle pass FNAB. 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 | 0.019 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".