Translating to the Community (T2C): a protocol paper describing the development of Canada’s first social epigenetic FASD biobank
Bibliographic record
Abstract
Translating to the Community (T2C) is a social biorepository designed to advance new diagnostic tools and realign community-clinical processes, with the aim to mitigate the short- and long-term impacts of fetal alcohol spectrum disorder (FASD) as well as prenatal alcohol exposure and its co-morbidities and behaviors. In this paper, we describe the evolution of this repository as a new translational partnership to advance a precision-medicine approach to FASD. Key to its evolution was a partnership between academic researchers, Indigenous communities, families, and a regional diagnostic clinic. We further describe the rationale for social biobanking, the type of banking, ethical engagement of families, communities, and clinics, their roles in repository design, governance, translation, and research activities, types of data collected from families, and how the study data are managed, reported, and accessed. The repository design includes biological samples, social-contextual health-survey data, and clinical data (which are linkable to administrative data) from community and clinical cohorts of diagnosed children, children prenatally exposed but not diagnosed, children suspected to have had a prenatal exposure, and related siblings, biological parents, and unrelated children and their parents. From these cohorts and families, potential studies drawing on this data will shed light on various risk factors, social and biological pathways, and service utilization issues, with the aim to implement primary and secondary prevention and intervention strategies.
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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 | 0.083 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.050 | 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".