Hyperactive behavior of athletes with post-concussion symptoms
Bibliographic record
Abstract
Objective Observations of hyperactive (/restless, agitated) behavior as a consequence of sport related concussions (SRC) are inconclusive as hypoactivity has also commonly been described. This might be grounded in the fact that the movement behavior of athletes has not been systematically investigated during standardized settings and with objective methods of movement analysis. Background Thus, we investigated the contradiction whether symptoms after SRC are characterized by a hyper- or hypoactive movement behavior experimentally. Design/Methods Three matched groups were investigated: 14 symptomatic and 14 asymptomatic athletes with a concussion; and 12 non-concussed athletes. Four certified raters analyzed with the NEUROGES the Activation and Contact incl. Rest/Pose categories as reliable measures of hypo/hyperactivity of (hand) / body movement activity that were displayed during a videotaped standardized anamnesis protocol. Results Symptomatic athletes spend significantly more time with act apart hand movements and less time with closed rest positions when compared to non-concussed athletes. Post-concussion symptom (PCS) scores positively correlate with act apart hand movements. A linear regression analysis revealed that act apart hand movements significantly predict the PCS score. Conclusions In line with previous descriptions of hyperactivity after SRC, athletes with increased symptoms after mTBI in sports behave hyperactive and restless when analyzed systematically. Because agitated/restless behavior was previously described in the concussed athletes who were later diagnosed with chronic traumatic encephalopathy (CTE), we suggest that future diagnoses should concern the detailed analysis of the movement activity as a potential behavioral marker of sport related concussions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".