Developing a Mobile App for Concussion to aid Patient Empowerment and Symptom Management
Bibliographic record
Abstract
Despite the high prevalence of concussions each year in Canada, access to consistent and science-based information on how to self-manage these injuries remains a significant hurdle for many patients. Currently, available mobile applications (apps) focus mainly on supporting patients with sports-related concussions, although falls account for more traumatic brain injuries (TBI) than sports-related TBI's in Alberta. Patients from a broader demographic may be limited from accessing information on how to correctly manage and track their symptoms as they feel that currently available resources are not applicable to them. Through collaboration between health system leaders, expert consultations, patients, and university students, a mobile app was designed as a platform to help patients manage and track symptoms at home, as well as to clarify misleading information and misconceptions surrounding injury. The team engaged numerous physicians, patient advisors, and health system leaders to improve upon the features of currently-existing concussion apps such as symptom tracking, insight into concussion, and strategies for returning to work/school that are more inclusive to adult, non-sports related injuries. We believe that these features will advance recovery by alleviating the burden of uncertainty and confusion for patients and their family members.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".