<i>DICER1</i> and Associated Conditions: Identification of At-risk Individuals and Recommended Surveillance Strategies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it