Genetic Counselor Recommendations for Cancer Predisposition Evaluation and Surveillance in the Pediatric Oncology Patient
Bibliographic record
Abstract
Abstract As the understanding of the genetic etiology of childhood cancers increases, the need for the involvement of experts familiar with the provision of genetic counseling for this population is paramount. In October 2016, the American Association for Cancer Research organized the AACR Childhood Cancer Predisposition Workshop in which international experts in pediatric cancer predisposition met to establish surveillance guidelines for children with cancer predisposition. Identifying for whom, when, why, and how these cancer predisposition surveillance guidelines should be implemented is essential. Genetic counselors invited to this workshop provide a genetic counseling framework for oncology professionals in this article. Points of entry and recommendations regarding the provision and timing of the initial and subsequent genetic counseling sessions are addressed. The genetic counseling and testing processes are reviewed, and the psychologic impact related to surveillance is explored. Pediatric cancer genetics will continue to grow and evolve as a field, and genetic counseling services will be vital to ensure appropriate identification and management of at-risk children moving forward. Clin Cancer Res; 23(13); e91–e97. ©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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".