fdg-pet in Two Cases of Neurofibromatosis Type 1 and Atypical Malignancies
Bibliographic record
Abstract
Patients with neurofibromatosis type 1 (nf1) are at increased risk for both benign and malignant tumours, and distinguishing the malignant potential of an individual tumour is a common clinical problem in these patients. Here, we review two cases of uncommon malignancies (Hodgkin lymphoma and mediastinal germ-cell tumour) in patients with nf1. Although (18)F-fluorodeoxyglucose positron-emission tomography (fdg-pet) has been used to differentiate benign neurofibromas from malignant peripheral nerve sheath tumours, fdg-pet characteristics for more rare tumours have been poorly described in children with nf1. Here, we report the role of pet imaging in clinical decision-making in each case. In nf1, fdg-pet might be useful in the clinical management of unusual tumour presentations and might help to provide information about the malignant potential of uncommon tumours.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.002 |
| 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".