Preliminary clinical algorithm to optimise remote delivery of paediatric concussion care in Canada’s North
Bibliographic record
Abstract
Concussion is a form of traumatic brain injury that affects thousands of children and adolescents across Canada annually. With timely access to comprehensive medical care, the majority of patients with acute concussion will recover within 1-4 weeks. Those who develop persistent post-concussion symptoms often benefit from early recognition and referral to multi-disciplinary concussion clinics that have the personnel and resources to meet their complex needs. Youth who live in remote and isolated communities within Canada's North, a significant proportion of whom are Indigenous, face unique barriers and obstacles to accessing primary and specialised concussion care. Although telemedicine has recently emerged as a tool that can help address these gaps in care, there are presently no clinical guidelines or tools available to assist multi-disciplinary concussion clinics in providing remote concussion care to these medically underserved patients. Here we incorporate literature from a scoping review and our early institutional experience to present an evidence-informed preliminary clinical algorithm and resources to help guide and optimise remote paediatric concussion care delivery in Canada's North. We also discuss how innovative technologies and partnerships can be leveraged to enhance the delivery of safe, equitable, cost-effective and culturally appropriate care to these communities.
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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 | 0.026 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".