TRANSCENDENT (Transforming Research by Assessing Neuroinformatics across the Spectrum of Concussion by Embedding iNterdisciplinary Data-collection to Enable Novel Treatments): protocol for a prospective observational cohort study of concussion patients with embedded comparative effectiveness research within a network of learning health system concussion clinics in Canada
Bibliographic record
Abstract
INTRODUCTION: Concussion affects over 400 000 Canadians annually, with a range of causes and impacts on health-related quality of life. Research to date has disproportionately focused on athletes, military personnel and level I trauma centre patients, and may not be applicable to the broader community. The TRANSCENDENT Concussion Research Program aims to address patient- and clinician-identified research priorities, through the integration of clinical data from patients of all ages and injury mechanisms, patient-reported outcomes and objective biomarkers across factors of intersectionality. Seeking guidance from our Community Advisory Committee will ensure meaningful patient partnership and research findings that are relevant to the wider concussion community. METHODS AND ANALYSIS: This prospective observational cohort study will recruit 5500 participants over 5 years from three 360 Concussion Care clinic locations across Ontario, Canada, with a subset of participants enrolling in specific objective assessments including testing of autonomic function, exercise tolerance, vision, advanced neuroimaging and fluid biomarkers. Analysis will be predicated on pre-specified research questions, and data shared with the Ontario Brain Institute's Brain-CODE database. This work will represent one of the largest concussion databases to date, and by sharing it, we will advance the field of concussion and prevent siloing within brain health research. ETHICS AND DISSEMINATION: This study was approved by the Children's Hospital of Eastern Ontario Research Ethics Board and preregistered on OSF (25 June 2024); https://doi.org/10.17605/OSF.IO/HYDZC. Dissemination of findings will be multifaceted, including conference presentations, peer-reviewed publications and sharing of adapted materials (eg, videos, infographics, plain language summaries) with community groups and key knowledge users.
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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 | 0.067 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.042 | 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".