Repeat Endoscopic Ultrasound-Guided Fine-Needle Aspiration in Patients with Suspected Pancreatic Cancer: Diagnostic Yield and Associated Change in Access to Appropriate Care
Bibliographic record
Abstract
Background. There is a high incidence of inconclusive cytopathology at initial EUS-FNA (endoscopic ultrasound-guided fine-needle aspiration) for suspected malignant pancreatic lesions. To obtain appropriate preoperative or palliative chemotherapy for pancreatic cancer, definitive cytopathology is often required. The utility of repeat EUS-FNA is not well established. Methods. A retrospective cohort study was conducted evaluating the yield of repeat EUS-FNA in determining a cytological diagnosis in patients who had undergone a prior EUS-FNA for diagnosis of suspected malignant pancreatic lesions with inconclusive cytopathology. The wait times to the second procedure and to decisions regarding therapy were calculated. Results. Overall, 45 repeat EUS-FNA procedures were performed over seven years for suspected malignant pancreatic lesions. Cytopathological class (I to IV) changed between first and second EUS-FNA in 32 patients (71%). Of 34 patients with an initially nonconclusive diagnosis, 20 had a conclusive diagnosis (59%) on repeat EUS-FNA. The cumulative yield after repeat EUS-FNA for definite pancreatic adenocarcinoma was 7 (16%). The median time interval between first and second EUS-FNA was 31 (7-175) days. Conclusions. A substantial number of patients had a definitive diagnosis of adenocarcinoma on repeat FNA and were, therefore, subsequently able to access appropriate care.
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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 | 0.001 | 0.015 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".