Hepatic Resection for Colorectal Liver Metastases and the Role of Positron Emission Tomography Imaging
Bibliographic record
Abstract
Colorectal cancer remains the third most prevalent cancer worldwide, contributing to over 600,000 deaths per year.In North America, colorectal cancer is the fourth most common newly diagnosed cancer each year.Colorectal cancer often metastasizes to the liver, which is best treated with a combination of surgical hepatic resection and chemotherapy.Unfortunately, only 20% of patients are candidates for surgical resection at presentation.Accurately determining resectability of hepatic metastatic disease is important prior to proceeding to surgery.The optimal imaging modality remains to be determined.Computed tomography (CT) and magnetic resonance imaging (MRI) have been the primary imaging modalities used to date to identify intrahepatic metastatic disease.Positron emission tomography (PET) imaging has been shown to increase sensitivity and specificity for detecting extrahepatic metastases.However, PET imaging is limited by the inability to accurately localize these lesions.Combined PET/CT imaging has been proposed as method to improve accuracy in detecting intra and extra-hepatic metastases.Current evidence is limited and further prospective studies are needed to clarify the role of PET/CT imaging in metastatic colorectal disease.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".