DICER1 syndrome: Approach to testing and management at a large pediatric tertiary care center
Bibliographic record
Abstract
BACKGROUND: To expand the current knowledge of DICER1 syndrome and to propose criteria for genetic testing based on experience at a pediatric tertiary care center. PROCEDURE: This study involved a retrospective chart review of the 78 patients (47 probands and 31 family members) seen in the Cancer Genetics Program at The Hospital for Sick Children (SickKids) who were offered genetic testing for DICER1. RESULTS: Of 47 probands offered genetic testing for DICER1, 46 pursued testing: 11 (23.9%) carried a pathogenic variant and one proband (2.1%) carried a missense variant of uncertain significance with evidence for pathogenicity. Thirty-one family members of variant-positive probands were offered testing: eight of the 25 who agreed to testing carried their familial variant (32.0%). Overall, 20 patients were identified to have a variant in DICER1 (eight males, 12 females). Of these, 13 (65.0%) presented with clinical manifestations associated with the syndrome. The most common lesions were pleuropulmonary blastoma (PPB) (five of 20 patients, 25.0%) and pineoblastoma (three of 20 patients, 15.0%). The average age at which individuals were diagnosed with a primary neoplasm was 5.2 years (range 0.8-20 years, median 3.0). Surveillance at our institution, with a median follow-up time of 23 months, has identified PPB in two asymptomatic individuals. These lesions were identified at early stages, thus potentially reducing treatment-related morbidity and mortality. CONCLUSION: This study further delineates the DICER1 syndrome phenotype and demonstrates the feasibility of a DICER1 syndrome surveillance protocol for the early detection of tumors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".