Changes in visuomotor performance of concussed individuals
Bibliographic record
Abstract
Recently, researchers have found evidence that after a concussion, residual visuo-motor control deficits may linger longer than working memory or psychomotor speed deficits. All of the major computer administered test batteries currently in use for concussion rehabilitation rely on examination of the latter kinds of tasks, and lack any measures of visuomotor control. The present research set out to develop a range of tasks which measure integrated visuomotor performance. Using a touch-screen computer, the first task required participants to point towards or away from (i.e., antipointing) a target. A second task required participants to intercept moving targets, and a third had participant's pointing to targets which moved in an unpredictable manner. All three required participants to use visual information to execute controlled movements, but ranged in the degree to which movement planning, early, or late guidance depended on visual information. The three tasks were delivered to 124 individuals from a healthy population to develop normative data for each of the measures. A self-report questionnaire was used to identify individuals from the normative population who had a prior history of concussion. Eighteen individuals were identified, and their performance was directly contrasted with the healthy individuals. While only a few reported moderate or severe concussions, and information about recency and number of occurrences were unavailable, performance differences were observed which provided evidence of residual deficits. In particular, while concussed individuals were not slower, or less accurate than the healthy population on the “pointing-antipointing” task, they demonstrated greater variability of performance. Future research will compare recently concussed individuals with the normative set developed here, and make direct comparisons with an existing computer administered test battery.
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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 | 0.001 |
| 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 | 0.001 |
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".