<i>DICER1</i> and Associated Conditions: Identification of At-risk Individuals and Recommended Surveillance Strategies
Bibliographic record
Abstract
Abstract Pathogenic germline DICER1 variants cause a hereditary cancer predisposition syndrome with a variety of manifestations. In addition to conferring increased cancer risks for pleuropulmonary blastoma (PPB) and ovarian sex cord–stromal tumors, particularly Sertoli–Leydig cell tumor, individuals with pathogenic germline DICER1 variants may also develop lung cysts, cystic nephroma, renal sarcoma and Wilms tumor, nodular hyperplasia of the thyroid, nasal chondromesenchymal hamartoma, ciliary body medulloepithelioma, genitourinary embryonal rhabdomyosarcoma, and brain tumors including pineoblastoma and pituitary blastoma. In May 2016, the International PPB Registry convened the inaugural International DICER1 Symposium to develop consensus testing and surveillance and treatment recommendations. Attendees from North America, Europe, and Russia provided expert representation from the disciplines of pediatric oncology, endocrinology, genetics, genetic counseling, radiology, pediatric surgery, pathology, and clinical research. Recommendations are provided for genetic testing; prenatal management; and surveillance for DICER1-associated pulmonary, renal, gynecologic, thyroid, ophthalmologic, otolaryngologic, and central nervous system tumors and gastrointestinal polyps. Risk for most DICER1-associated neoplasms is highest in early childhood and decreases in adulthood. Individual and caregiver education and judicious imaging-based surveillance are the primary recommended approaches. These testing and surveillance recommendations reflect a consensus of expert opinion and current literature. As DICER1 research expands, guidelines for screening and treatment will continue to be updated. Clin Cancer Res; 24(10); 2251–61. ©2018 AACR.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.006 | 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".