ATRT-13. CANCER PREDISPOSITION AMONG CHILDREN WITH RHABDOID TUMORS: A SINGLE-CENTRE RETROSPECTIVE REVIEW
Bibliographic record
Abstract
Rhabdoid tumor predisposition syndrome (RTPS) from SMARCB1 germline mutations must be ruled out in children with rhabdoid tumors (RT). The aim was to describe the diagnostic details of children with RT, genetic referral practices, and rates of SMARCB1 germline pathogenic mutations, and to compare clinical features of children with and without RTPS. The medical charts of sequential children diagnosed with RT at the Hospital for Sick Children from 1995–2016 were reviewed for the diagnostic details, family histories, and genetic testing results. Fifty-nine children diagnosed with RT at a mean age of 37.6 months were included. Atypical teratoid rhabdoid tumors represented 73% of the tumors. Of the patients with family histories documented, 17% were suspicious for cancer predisposition. Of the 31 patients with genetic testing results, 12 had SMARCB1 pathogenic mutations. The mean age of diagnosis was 12.0 months for children with RTPS compared to 37.6 months in those without RTPS. In children presenting at ≤ 12 months of age, 64% were subsequently diagnosed with germline SMARCB1 mutations. When the age limit was increased to ≤ 36 months, the rate of SMARCB1 mutations was 42%, while this rate was 25% in those aged > 36 months. Rates of SMARCB1 germline mutations increase with younger age, but the elevated rates of RTPS detection in this cohort for all ages support the practice that all children diagnosed with RT should undergo genetic testing to investigate for RTPS, even if the family history is not suggestive of inherited cancer.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".