<i>PTEN, DICER1, FH</i>, and Their Associated Tumor Susceptibility Syndromes: Clinical Features, Genetics, and Surveillance Recommendations in Childhood
Bibliographic record
Abstract
Abstract PTEN hamartoma tumor syndrome (PHTS), DICER1 syndrome, and hereditary leiomyomatosis and renal cell cancer (HLRCC) syndrome are pleiotropic tumor predisposition syndromes that include benign and malignant neoplasms affecting adults and children. PHTS includes several disorders with shared and distinct clinical features. These are associated with elevated lifetime risk of breast, thyroid, endometrial, colorectal, and renal cancers as well as melanoma. Thyroid cancer represents the predominant cancer risk under age 20 years. DICER1 syndrome includes risk for pleuropulmonary blastoma, cystic nephroma, ovarian sex cord–stromal tumors, and multinodular goiter and thyroid carcinoma as well as brain tumors including pineoblastoma and pituitary blastoma. Individuals with HLRCC may develop multiple cutaneous and uterine leiomyomas, and they have an elevated risk of renal cell carcinoma. For each of these syndromes, a summary of the key syndromic features is provided, the underlying genetic events are discussed, and specific screening is recommended. Clin Cancer Res; 23(12); e76–e82. ©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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".