Canadian Open Genetics Repository (COGR): a unified clinical genomics database as a community resource for standardising and sharing genetic interpretations
Bibliographic record
Abstract
BACKGROUND: The Canadian Open Genetics Repository is a collaborative effort for the collection, storage, sharing and robust analysis of variants reported by medical diagnostics laboratories across Canada. As clinical laboratories adopt modern genomics technologies, the need for this type of collaborative framework is increasingly important. METHODS: A survey to assess existing protocols for variant classification and reporting was delivered to clinical genetics laboratories across Canada. Based on feedback from this survey, a variant assessment tool was made available to all laboratories. Each participating laboratory was provided with an instance of GeneInsight, a software featuring versioning and approval processes for variant assessments and interpretations and allowing for variant data to be shared between instances. Guidelines were established for sharing data among clinical laboratories and in the final outreach phase, data will be made readily available to patient advocacy groups for general use. RESULTS: The survey demonstrated the need for improved standardisation and data sharing across the country. A variant assessment template was made available to the community to aid with standardisation. Instances of the GeneInsight tool were provided to clinical diagnostic laboratories across Canada for the purpose of uploading, transferring, accessing and sharing variant data. CONCLUSIONS: As an ongoing endeavour and a permanent resource, the Canadian Open Genetics Repository aims to serve as a focal point for the collaboration of Canadian laboratories with other countries in the development of tools that take full advantage of laboratory data in diagnosing, managing and treating genetic diseases.
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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 | 0.032 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.024 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.011 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.011 |
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".