What evidence exists for new strategies or technologies in the diagnosis of sports concussion and assessment of recovery?
Bibliographic record
Abstract
OBJECTIVE: The purpose of this critical review is to summarise the evidence for the following technologies/strategies related to diagnosing or managing sports-related concussion: quantitative EEG, functional neuroimaging, head impact sensors, telemedicine and mobile devices. DATA SOURCES: MEDLINE, PubMed, Cochrane Controlled Trials Registers, SportDiscus, EMBASE, Web of Science and ProQuest databases. Primary search keywords were concussion, sports concussion and mild traumatic brain injury. The keywords used for secondary, topic specific searches were quantitative electroencephalography, qEEG, functionalMRI, magnetoencephalography, near-infrared spectroscopy, positron emission tomography, single photon emissionCT, accelerometer, impact sensor, telemetry, remote monitoring, robotic medicine, telemedicine, mobile device, mobile phone, smart phone and tablet computer. RESULTS: The primary search produced 8567 publications. The secondary searches produced nine publications that presented original data, included a comparison group in the study design and involved sports-related concussion. Four studies spoke to the potential of qEEG as a diagnostic or management tool, while five studies addressed the potential of fMRI to be used in the same capacity. CONCLUSIONS: Emerging technologies and novel approaches that aid in sports concussion diagnosis and management are being introduced at a rapid rate. While some technologies show promise, their clinical utility remains to be established.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".