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 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 | 0.006 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".