Manual Dexterity in Patients with Disorder of Consciousness
Bibliographic record
Abstract
Objective To examine hand motor features in individuals diagnosed with disorder of consciousness (DoC) after severe brain injury, and to investigate the relationship between manual dexterity and levels of consciousness. Design Cross-sectional study. Setting Rehabilitation hospital. Participants Sixty patients with DoC (44 men and 16 women), with a mean age of 55.03±15.28 years and a mean postinjury duration of 10.68±7.98 months. Interventions Not applicable (no interventions were administered). Main Outcome Measures Manual dexterity (Brunnstrom Recovery Stage [BRS]), spasticity (Modified Ashworth Scale), pain (Nociception Coma Scale-Revised), and consciousness (Coma Recovery Scale-Revised) were assessed. Descriptive, nonparametric, and multivariate analyses were performed. Results Evaluation of the best-functioning hands in the 60 patients with DoC showed that 60% (n=36) had low dexterity (BRS<4), and edema was revealed in 28.3% (17/60) of these hands. Compared with patients in minimally conscious state (MCS), those in unresponsive wakefulness syndrome (UWS) had significantly lower manual dexterity (96.2% vs 32.4%) and a higher prevalence of hand edema (42.3% vs 17.6%). BRS scores were significantly higher for hands than for arms ( P <.001). Manual dexterity was strongly associated with consciousness diagnosis (adjusted odds ratios=16.03, 95% confidence interval, 2.16-119.11; P =.007). No significant difference was found between right and left hands. Conclusions A considerable proportion of patients with DoC experience severe hand complications, which can negatively impact their clinical diagnosis and quality of life. The BRS is effective in detecting subtle hand motor responses that may reflect residual consciousness in DoC. The observed association between manual dexterity and consciousness suggests that hand motor control may engage neural circuits involved in consciousness processing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".