Comparison of imaging-based and pathological dimensions in pancreatic neuroendocrine tumors
Bibliographic record
Abstract
AIM: To establish the ability of magnetic resonance (MR) and computer tomography (CT) to predict pathologic dimensions of pancreatic neuroendocrine tumors (PanNET) in a caseload of a tertiary referral center. METHODS: Patients submitted to surgery for PanNET at the Surgical Unit of the Pancreas Institute with at least 1 preoperative imaging examination (MR or CT scan) from January 2005 to December 2015 were included and data retrospectively collected. Exclusion criteria were: multifocal lesions, genetic syndromes, microadenomas or mixed tumors, metastatic disease and neoadjuvant therapy. Bland-Altman (BA) and Mountain-Plot (MP) statistics were used to compare size measured by each modality with the pathology size. Passing-Bablok (PB) regression analysis was used to check the agreement between MR and CT. RESULTS: = 0.433). PBR analysis did not reveal significant differences between MR, CT and pathology. CONCLUSION: MR and CT scan are accurate and interchangeable imaging techniques in predicting pathologic dimensions of PanNET.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".