Abstract SY09-03: PROFYLEing Cancer for KiCS: The Canadian Pediatric Precision Oncology Initiative
Bibliographic record
Abstract
Abstract Each year, approximately 4300 children, adolescents and young adults (CAYA) are diagnosed with cancer. One-third of these patients present with metastatic disease, develop refractory disease or relapse. For these patients, the likelihood of survival remains grim and essentially unchanged in more than three decades. Precision Oncology for Young PeopLE (PROFYLE) is a pan-Canadian, interdisciplinary program that was built on the foundation of three major sequencing efforts in Vancouver (PedsPOG), Toronto (KiCS) and Montreal (TRICEPS) for children with hard-to-treat cancer. Since its inception in 2017, over 700 patients have had complete NGS of paired blood-tumor samples with a goal to not only develop a national precision oncology pipeline, but also to determine the frequency and spectrum of molecular targets for novel therapies and other clinically actionable findings. During this presentation, both the process as well as current findings will be presented and plans for the future will be outlined. Citation Format: David Malkin, Jason N. Berman, Jennifer A. Chan, Avram Denburg, Rebecca Deyell, David Eisenstadt, Conrad Fernandez, Stephanie Grover, Abha Gupta, Cynthia Hawkings, Meredith Irwin, Nada Jabado, Steven Jones, Daniel Morgenstern, Michael Moran, Rod Rasesekh, Adam Shlien, Daniel Sinnett, Poul Sorensen, Patrick Sullivan, Michael Taylor, Anita Villani, Jim Whitlock. PROFYLEing Cancer for KiCS: The Canadian Pediatric Precision Oncology Initiative [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr SY09-03.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.068 | 0.016 |
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".