Role of pre-operative inflammatory markers as predictors of lymph node positivity and disease recurrence in well-differentiated pancreatic neuroendocrine tumours. Study group affiliation: Pancreas2000 Research and educational program (course 9)
Bibliographic record
Abstract
Diagnosis of pancreatic exocrine insufficiency (PEI) is hindered by methodological difficulties of pancreatic function tests. The probability of PEI in chronic pancreatitis (CP) increases as pancreatic fibrosis develops. Pancreatic fibrosis in CP may be quantified by EUS elastography.To evaluate whether EUS-elastography can predict PEI in patients with CP.Prospective, observational study.Department of Gastroenterology, University Hospital of Santiago de Compostela, Spain.Patients diagnosed with CP based on EUS and magnetic resonance imaging and MRCP findings.Diagnosis of PEI was based on the 13C-mixed triglyceride breath test. EUS-elastography was performed with PENTAX echoendoscopes and Hitachi-Preirus US platform. Two areas were selected for elastographic evaluation: area A corresponds to the pancreatic parenchyma and area B to a soft peripancreatic reference area. The quotient B/A (strain ratio [SR]) was considered the elastographic result.Pancreatic SR in CP patients with and without PEI.A total of 115 patients with CP (mean age, 50.2 years, range, 21-81; 92 male) of different etiologies were included; 35 patients (30.4%) had PEI. Pancreatic SR was higher in patients with PEI (4.89; 95% confidence interval, 4.36-5.41) than in those with a normal breath test result (2.99; 95% confidence interval, 2.82-3.16) (P < .001). A direct relationship was found between the SR and the probability of PEI, which increases from 4.2% in patients with an SR less than 2.5 to 92.8% in those with an SR greater than >5.5.Single-center study.The degree of pancreatic fibrosis as measured by EUS-guided elastography allows quantification of the probability of PEI in patients with CP.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".