Determining when endoscopic ultrasound (EUS) changes management for patients with pancreatic cystic neoplasms.
Bibliographic record
Abstract
641 Background: Pancreatic cystic neoplasms (PCNs) are being incidentally detected at an increased rate due to the widespread use of CT and MRI. CT and MRI cannot always differentiate between malignant and benign PCNs. EUS is an emerging tool that provides higher quality descriptions of pancreatic cysts and can be used to differentiate between benign and malignant features. Considering that EUS is a resource dependent tool, we hope to identify the PCN cases in which EUS changes management. Methods: We conducted a retrospective case-control chart review evaluating patients, who were diagnosed with pancreatic cysts and underwent EUS for analysis between January 1, 2010 and December 31, 2017. We determined whether EUS correctly identified high-risk features (HRFs) relative to CT/MRI and whether EUS upstaged or downstaged the CT/MRI diagnosis to change overall patient management. Results: EUS was found to have a high specificity (> 95%) for all high-risk features identified in the AGA and FG guidelines and a low sensitivity ( < 70%) for all high risk features except cyst size > 3cm (82.35%) and mural nodule < 5mm (100%). EUS was found to change management in 29.4% of cases (18.2% upstaged, 11.2% downstaged). EUS screening led to a total of three adenocarcinoma diagnoses, in which two were reported to be invasive. Conclusions: The high specificity of EUS supports its use in the differentiation of high risk PCNs identified on cross-sectional imaging. Its low sensitivity indicates that the reliance on operator experience may be a substantial limitation resulting in inconclusive diagnoses. In conclusion, considering that EUS is successful in changing patient management of PCNs, it should be readily referred when any HRF is identified on cross-sectional imaging.
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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 | 0.011 |
| 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 | 0.000 | 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".