Smartphone and tablet apps for concussion road warriors (team clinicians): a systematic review for practical users
Bibliographic record
Abstract
BACKGROUND: Mobile technologies are steadily replacing traditional assessment approaches for the recognition and assessment of a sports concussion. Their ease of access, while facilitating the early identification of a concussion, also raises issues regarding the content of the applications (apps) and their suitability for different user groups. AIM: To locate and review apps that assist in the recognition and assessment of a sports concussion and to assess their content with respect to that of internationally accepted best-practice instruments. METHODS: A search of international app stores and of the web using key terms such as 'concussion', 'sports concussion' and variants was conducted. For those apps meeting the inclusion criteria, data were extracted on the platform, intended users and price. The content of each app was benchmarked to the Sport Concussion Assessment Tool 2 (SCAT2) and Pocket SCAT2 using a custom scoring scheme to generate a percentage compliance statistic. RESULTS: 18 of the 155 apps identified met the inclusion criteria. Almost all (16/18) were available on an iOS platform and only five required a payment to purchase. The apps were marketed for a wide range of intended users from medical professionals to the general public. The content of the apps varied from 0% to 100% compliance with the selected standard, and 'symptom evaluation' components demonstrated the highest level of compliance. CONCLUSIONS: The surge in availability of apps in an unregulated market raises concerns as to the appropriateness of their content for different groups of end users. The consolidation of best-practice concussion instruments now provides a framework to inform the development of future apps.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".