Selection of Patients for Resection of Hepatic Metastases: Improved Detection of Extrahepatic Disease with FDG PET
Bibliographic record
Abstract
A rapidly emerging clinical application of positron emission tomography (PET) is the detection of tumor tissue at whole-body studies performed with the glucose analogue 2-[fluorine-18]fluoro-2-deoxy-D-glucose (FDG). High rates of recurrence after partial hepatic resection in patients with colorectal cancer liver metastases indicate that current presurgical imaging strategies are failing to show extrahepatic tumor deposits. Although FDG PET cannot match the anatomic resolution of conventional imaging techniques in the liver and the lungs, it is particularly useful for identification and characterization of extrahepatic disease. FDG PET can show foci of metastatic disease that may not be apparent at conventional anatomic imaging and can aid in the characterization of indeterminate soft-tissue masses. Several sources of benign and physiologic increased activity at FDG PET emphasize the need for careful correlation with findings of other imaging studies and clinical findings. FDG PET can improve the selection of patients for partial hepatic resection and thereby reduce the morbidity and mortality associated with inappropriate surgery.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".