OCTANE (Ontario-Wide Cancer Targeted Nucleic Acid Evaluation): A Platform for Intraprovincial, National, and International Clinical Data-Sharing
Bibliographic record
Abstract
Cancer is a genetic disease resulting from germline or somatic genetic aberrations. Rapid progress in the field of genomics in recent years is allowing for increased characterization and understanding of the various forms of the disease. The Ontario-wide Cancer Targeted Nucleic Acid Evaluation (octane) clinical trial, open at cancer centres across Ontario, aims to increase access to genomic sequencing of tumours and to facilitate the collection of clinical data related to enrolled patients and their clinical outcomes. The study is designed to assess the clinical utility of next-generation sequencing (ngs) in cancer patient care, including enhancement of treatment options available to patients. A core aim of the study is to encourage collaboration between cancer hospitals within Ontario while also increasing international collaboration in terms of sharing the newly generated data. The single-payer provincial health care system in Ontario provides a unique opportunity to develop a province-wide registry of ngs testing and a repository of genomically characterized, clinically annotated samples. It also provides an important opportunity to use province-wide real-world data to evaluate outcomes and the cost of ngs for patients with advanced cancer. The octane study is attempting to translate knowledge to help deliver precision oncology in a Canadian environment. In this article, we discuss the background to the study and its implementation, current status, and future directions.
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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 | 0.028 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".