Endoscopic Ultrasound in Nova Scotia: A Quality Assurance Study
Bibliographic record
Abstract
Introduction: Since the single most important function of EUS is in its ability to obtain tissue via FNA, our primary outcome measure will be yield of FNA for the various indications. Secondary outcome measures will include the indications, complications and waiting time. We also compared the diagnostic yield of FNA with and without having Rapid on-site Evaluation (ROSE). This quality assurance study will help in improving the EUS program in our province. Methods: It is an observational, retrospective cohort study of all the men and women who had undergone EUS in Nova Scotia. Subjects of this research consist of 176 patients. 114 EUS reports, including 52 FNA without ROSE, will be analyzed to determine the reason for referral to EUS, the complications if any, and the waiting time for an EUS appointment in the out patients sittings. Results of EUS with or without FNA will be charted as well as the diagnosis obtained via cytological analysis. Another 60 EUS reports were FNA was taken with ROSE, were analyzed and diagnostic yields were charted and compared with the FNAs without ROSE. Results: The most common reasons for referral to EUS were for evaluation of pancreatic mass/cyst, mediastinal mass/lymph node and assessment of sub-mucosal lesions. Other indications were dilated CBD, pancreatic cancer screening, chronic unexplained pancreatitis. A total of 110 FNA's (52 without ROSE, and 60 with ROSE) were performed by EUS for different indications; most of them were from a pancreatic mass/cyst, Lymph node and submucosal lesions. In the FNAs without ROSE, 86% of results were conclusive, compared to 82% with ROSE. Among the FNA obtained from a solid pancreatic mass, 88% were conclusive without ROSE compared to 86% with ROSE. The most common abnormal FNA results from the pancreas were pancreatic adenocarcinoma. Other results included pancreatic lymphoma, metastatic malignancy from lymph node FNA, and lung cancer. 2 patients (1.7%) developed complications post EUS/FNA. Conclusion: EUS can be used for variety of indications, most commonly to further characterize a pancreatic lesion, with the ability of obtaining a tissue diagnosis through FNA with good diagnostic yield that guided patients management. It is a minimally invasive procedure with low complication rate. In our observational study, ROSE didn't increase the diagnostic yield of FNA, although further studies with RCT needed to establish the efficacy of ROSE in sensitivity and specificity as well.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".