Balance impairment in patients with moderate-to-severe traumatic brain injury: Which measures are appropriate for assessment?
Bibliographic record
Abstract
Left untreated, balance impairment following moderate-to-severe traumatic brain injury (TBI) can be highly debilitating and hinder activities of daily life. To detect impairments, clinicians need appropriate assessment tools. The objective of this study was to evaluate the feasibility and utility of a battery of clinical balance assessments in adults with moderate-to-severe TBI within 6-months of injury. Thirty-seven adults with TBI [Glasgow Coma Scale score ≤ 12 (33 M/4 F) age 18-50 years] participated in balance testing. Assessments included the Balance Error Scoring System (BESS), National Institutes of Health Standing Balance Test (NIH-SBT), Functional Gait Assessment (FGA), Advanced Functional Gait Assessment (FGA-A), Tandem Gait Test (TGT), Berg Balance Scale (BBS), and Walking While Talking Test (WWTT). We identified pronounced ceiling effects on the BBS and FGA, two widely used clinical balance assessments. The NIH-SBT, WWTT, and FGA used in conjunction with the FGA-A, offered versatility in their capacity to assess patients across the balance severity spectrum. This study provides evidence to support a stepwise approach to balance assessment that can be adapted to the broad range of balance ability found in moderate-to-severe TBI.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".