Nonverbal hand movement durations indicate post-concussion symptoms of athletes
Bibliographic record
Abstract
Objective Concussions are common in sports and appear to be a risk factor for cognitive impairment and mental health problems. Methods of post-concussion diagnosis are still of debate, regarding sensitivity, objectivity, reliability, and costs. Spontaneous displays of nonverbal hand movement behavior during interaction are indicative of psychopathology and relatively simple to record and analyze. Background Increased durations of continuous (/irregular) body-focused hand movement activity in particular indicates psychopathology that overlaps in symptomatology with that of sport related concussions (SRC). We therefore hypothesized that the duration of irregular, on body, and act on each other hand movements is increased in athletes with SRC who suffer from post-concussion symptoms. Design/Methods Three matched groups were investigated: 14 symptomatic athletes with a concussion, 14 asymptomatic athletes with a concussion, and 12 non-concussed athletes. Four certified raters analyzed with the NEUROGES analysis system all nonverbal hand movements that were displayed during a videotaped standardized anamnesis about concussion history, incidence, course of action, and post-concussion symptoms. Results Irregular Structure units of symptomatic athletes are significantly longer when compared to asymptomatic athletes. Hand movement durations of irregular, on body, and act on each other correlate positively with post-concussion symptoms. The duration of irregular units significantly predicts the PCS score. Conclusions Increased durations of irregular hand movement units indicate post-concussion symptoms in athletes with sport-related concussions. Because the recording of spontaneous displays of nonverbal hand movement behavior is relatively simple and cost-efficient, we suggest using the neuropsychological analysis of hand movement behavior as a future diagnostic parameter of concussion management protocols.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".