Update on Surveillance in Von Hippel–Lindau Disease
Bibliographic record
Abstract
Von Hippel-Lindau disease (VHL) is a genetic condition characterized by a high lifetime risk for tumors and cysts throughout the body, including the central nervous system, visual-auditory systems, and intra-abdominal organs. This neoplasia leads to significant morbidity and potential mortality in affected individuals. Tumor surveillance enables early intervention and leads to improved clinical outcomes. Since the 2017 publication of VHL tumor surveillance recommendations from the inaugural American Association for Cancer Research Childhood Cancer Predisposition Workshop, several other groups have proposed alternative consensus surveillance recommendations. Although these screening paradigms share some common elements, they also deviate from each other in some substantial ways. Clinical data continue to accrue in VHL, allowing the condition to be better characterized. Furthermore, surgical techniques have improved over time, and the option of targeted medical therapy has emerged for individuals with VHL. It is critical that surveillance strategies continue to be refined. In this perspective, we provide an up-to-date clinical overview of VHL, describe recently proposed tumor screening regimens, and finally present our updated consensus tumor surveillance recommendations during childhood and adolescence from the 2023 American Association for Cancer Research Childhood Cancer Predisposition Workshop.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".