The necessity of using baseline data to determine balance recovery post-concussion during the asymptomatic phase
Bibliographic record
Abstract
Objective To determine whether sensory manipulation balance testing was able to detect balance control impairments in previously concussed asymptomatic (PCA) athletes. We hypothesised that PCA athletes would have balance control deficits in the A/P direction with sensory manipulations. Participants 27 female athletes (19.78±1.3 years); 13 were previously concussed (PC) within the past 12 months and 14 had no history of concussions (NC). Design Manipulation of visual, somatosensory, and vestibular contribution to balance control within the following trials: 3 eyes open (EO); 3 eyes closed (EC); 1 dual-task arithmetic (DUAL); compliant surface with eyes open (EOc) and eyes closed (ECc); 1 horizontal eye movement task (VOR); and 1 horizontal head turns with EC (HHT). Participants tested prior to the start of season; four of them sustained a concussion during the season and were retested following the resolution of symptoms (i.e., RTP-2). Procedures Participants stood for 45s on a Bertec force plate (collected at 100Hz) in a Romberg stance. Trials were presented in order of increasing difficulty: EO1, EC1, dual task, EOc, ECc, VOR, EO2, EC2, HHT, EO3, and EC3. Data analysis Root mean square (RMS) of centre of pressure displacement and velocity (dCOP and vCOP) were calculated in the A/P and M/L directions. Statistical analysis Independent (PC vs. NC) and paired (baseline vs. RTP-2) t-tests were performed for each condition in both directions for dCOP and vCOP. Main results HHT condition revealed that A/P vCOP RMS (mm/s) was significantly greater in PC (Mean Diff=2.63, p=0.02) and within at RTP-2 (Mean Diff=5.24, p=0.02). Conclusions Balance impairments exist up to 12 months post-concussion in PCA athletes when their vestibular systems were perturbed (HHT) and higher-order analyses (vCOP) were calculated. Competing interests None.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".