Cancer Surveillance in Gorlin Syndrome and Rhabdoid Tumor Predisposition Syndrome
Bibliographic record
Abstract
Abstract Gorlin syndrome and rhabdoid tumor predisposition syndrome (RTPS) are autosomal dominant syndromes associated with an increased risk of childhood-onset brain tumors. Individuals with Gorlin syndrome can manifest a wide range of phenotypic abnormalities, with about 5% of family members developing medulloblastoma, usually occurring in the first 3 years of life. Gorlin syndrome is associated with germline mutations in components of the Sonic Hedgehog pathway, including Patched1 (PTCH1) and Suppressor of fused (SUFU). SUFU mutation carriers appear to have an especially high risk of early-onset medulloblastoma. Surveillance MRI in the first years of life in SUFU mutation carriers is, therefore, recommended. Given the risk of basal cell carcinomas, regular dermatologic examinations and sun protection are also recommended. Rhabdoid tumors (RT) are tumors initially defined by the descriptive “rhabdoid” term, implying a phenotypic similarity with rhabdomyoblasts at the microscopic level. RTs usually present before the age of 3 and can arise within the cranium as atypical teratoid/rhabdoid tumors or extracranially, especially in the kidney, as malignant rhabdoid tumors. However, RTs of both types share germline and somatic mutations in SMARCB1 or, more rarely, SMARCA4, each of which encodes a chromatin remodeling family member. SMARCA4 mutations are particularly associated with small cell carcinoma of the ovary, hypercalcemic type (SCCOHT). The outcome following a diagnosis of any of these tumors is often poor, and the value of surveillance is unknown. International efforts to determine surveillance protocols are underway, and preliminary recommendations are made for carriers of SMARCB1 and SMARCA4 mutations. Clin Cancer Res; 23(12); e62–e67. ©2017 AACR. See all articles in the online-only CCR Pediatric Oncology Series.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".