The Impact of Preoperative Endoscopic Ultrasound on the Surgical Management of Pancreatic Neuroendocrine Tumours
Bibliographic record
Abstract
BACKGROUND: Endoscopic ultrasound (EUS) is accurate in diagnosing pancreatic neuroendocrine tumours (PNETs), but its impact on surgical management is unclear. OBJECTIVE: To determine whether preoperative EUS findings altered the decision for, and extent of, surgery in patients with PNETs. METHODS: A retrospective review of patients referred for EUS because of suspected PNETs was conducted. The diagnosis of PNETs was confirmed by EUS-guided fine needle aspiration cytology, where indicated, or by surgical histology. EUS findings were compared with computed tomography (CT) findings to determine whether there was an impact on the decision for surgical management. RESULTS: Fourteen patients (10 women), with a mean age of 44 years, underwent EUS for suspected PNETs. PNETs were seen with CT in 10 of 13 patients (77%) and with EUS in 14 of 14 patients (100%). One obese patient could not fit into the CT scanner. This patient had five PNETs on EUS. Three patients with a normal CT scan were determined to have one or two PNETs on EUS. Three patients with one or two PNETs on CT were found to have five to eight PNETs on EUS. EUS altered the decision for possible surgical management in five of 14 patients (36%), either by identifying a PNET or by finding multiple and multifocal PNETs that were not visualized on CT scans. CONCLUSION: EUS is useful in the preoperative assessment of PNETs by providing information that significantly influences the decision for surgical intervention or changes the extent of the planned surgery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".