Development of the Ontario Hereditary Cancer Research Network, a unified registry as a resource for individuals with inherited cancer syndromes: an observational registry creation protocol
Bibliographic record
Abstract
INTRODUCTION: In Canada, care for individuals with hereditary cancer is fragmented across the provinces and territories, with carriers of pathogenic variants in cancer-susceptibility genes seeing multiple doctors and often advocating for their own management plans. The need for a national registry of carriers has been well established. While other cancer consortia exist, barriers in clinical and genomic data sharing limit the utility of the information gathered. METHODS AND ANALYSIS: Within the province of Ontario, the Ontario Hereditary Cancer Research Network (OHCRN), funded by and located at the Ontario Institute for Cancer Research, is being developed to fill this gap. The registry will hold clinical, genomic and self-reported data from consented carriers and will make this data available to qualified researchers in anonymised and aggregated form. Individuals must agree to certain components to participate in OHCRN; there are also optional consents participants can agree to without impacting their involvement in OHCRN. We plan to open the registry for participant enrolment in mid-2025. ETHICS AND DISSEMINATION: Ethics approval for registry creation was obtained from the Ontario Cancer Research Ethics Board, a centralised body that streamlines reviews for cancer research studies in Ontario. Registry data will be disseminated to participants and researchers as aggregate data through the OHCRN website and presented at scientific conferences, made available to Ontario Health (Cancer Care Ontario) to inform policy and evidence-based practice, as well as be available to the scientific community for further analysis and answering relevant questions.
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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 | 0.124 | 0.117 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.010 |
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".