Assessment of Postural Stability During an Upper Extremity Rapid, Bimanual Motor Task After Sport-Related Concussion
Bibliographic record
Abstract
CONTEXT: Sport-related concussion (SRC) often presents with multidimensional and subtle neurologic deficits that are difficult to detect with standard clinical tests. New assessment approaches that efficiently quantify deficits across multiple neurologic domains are needed. OBJECTIVE: To quantify impairments in postural movements during an assessment of rapid, bimanual motor ability in athletes within 10 days of experiencing an SRC and evaluate relationships between impairments in upper extremity and postural performance. DESIGN: Cohort study. SETTING: Sports medicine clinic. PATIENTS OR OTHER PARTICIPANTS: Initial baseline assessments were completed for 711 athletes. Seventy-five athletes (age = 15.8 ± 3.3 years at baseline) sustained SRCs and were reassessed within 10 days. Seventy-eight athletes (age = 15.5 ± 2.0 years) completed 2 assessments in a healthy state. MAIN OUTCOME MEASURE(S): Athletes stood on force plates and performed a rapid, bimanual motor task, termed the object-hit task, delivered using a Kinesiological Instrument for Normal and Altered Reaching Movements endpoint robot. Measures of postural stability that quantified center-of-pressure movements and measures of upper extremity performance were used to characterize task performance. RESULTS: Performance changes across assessments were converted to reliable change indices. We observed a difference in reliable change indices values between athletes with SRC and healthy control athletes on the combined postural measures (P = .01). Using measures to evaluate the change in postural movements from the early, easier portion of the task to the later, more difficult portion, we identified the highest levels of impairment (19%-25% of the sample impaired). We also noted a difference between individuals with concussion and healthy individuals on the combined upper extremity measures (P = .003), but these impairments were largely unrelated to those identified in the postural movements. CONCLUSIONS: Measurement of postural movements during the object-hit task revealed impairments in postural stability that were not related to impairments in upper extremity performance. The findings demonstrated the benefits of using assessments that simultaneously evaluate multiple domains of neurologic function (eg, upper extremity and postural control) after SRC.
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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 | 0.001 | 0.002 |
| 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".