Implementing a consultation service for translating genomic research findings into the clinic: lessons from the SickKids Genome Board
Bibliographic record
Abstract
ABSTRACT Objectives Genome-wide sequencing (GWS) is now used across the breadth of paediatric research. There is a greater potential to identify unexpected, clinically relevant findings with GWS than with the targeted genetic techniques used in prior decades. Individual research teams may not have the expertise to evaluate and manage these findings. The Hospital for Sick Children (SickKids) Genome Board is a no-cost consultation service for researchers with questions arising from genetic aspects of their studies. Methods We reviewed all submissions to and recommendations from the Genome Board over the first four years, to identify common questions, themes, and trends. Results There were 67 submissions, and a year-over-year increase in volumes. The most common request (60%) was to assess variants identified by GWS for pathogenicity, clinical actionability, and returnability to a study participant. Overall, 23 of 48 reviewed variants were recommended for clinical confirmation and return with genetic counselling. Other categories of submissions included requests to researchers from study participants to release their “raw” genomic data, and for input on protocols related to clinical translation of findings. Conclusion The Genome Board provides a generalizable model for centralized triage of clinical questions arising from genomic research at a paediatric centre. Clinicians should be aware that patient participation in genetic research studies can have downstream consequences for their healthcare.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.159 | 0.263 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".