Feasibility of At-Home Vibrotactile Data Collection in Children and Adolescents: Study of Mild Traumatic Brain Injury
Bibliographic record
Abstract
Remote testing has become a desireable option as it helps reduce participant burden, can be more convenient and enables longitudinal data collection to track symptom recovery. Recently, advances in testing have enabled researchers to test somatosensory processing and brain function. Using tactile testing modalities such as vibrotactile stimulation to the fingertips can provide information about cortical inhibition, for example, without the need for invasive testing procedures. In the current manuscript, we present our initial experience for ‘at home’ tactile testing. We demonstrate 1) it is possible to develop an ‘at home’ testing battery with multiple tasks that is comparable to ‘in lab’ testing; and 2) it is feasible to collect this data remotely and repeatedly to monitor longitudinal changes. Participants included pediatric concussion patients and orthopedic injury (OI) controls, 8-18 years of age at time of participation, and were recruited ~10 days after injury. Testing was conducted on a 2-digit vibrotactile stimulator hand-held device and was based on previously used protocols. Stimulation was delivered to the left index and middle finger. Data quality of tasks was visually inspected to ensure data followed a pattern of converging values of thresholds over time. A total of 19 participants were recruited in this study; 11 concussion and 8 OI. Participants in the concussion group were 12.8 ± 2.2 years old (36.4% female) and participants in the OI group were 11.6 ± 2.5 years old (57.1% female) at the time of injury. Results from paired sample t-tests comparing task performance did not detect significant differences between the data collected from the home session and at the lab visit for the concussion group. Our results demonstrate that vibrotactile sensory testing can provide a non-invasive, objective measure of central nervous system functioning without relying on subjective questionnaires. This work demonstates it is possible to perform this testing remotely. Our data with children and adolescents demonstrates they are capable of completing these tasks at home; we therefore expect this at home testing protocol could easily be administered in other populations.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".