Defining the Role of Sensation, Strength, and Prehension for Upper Limb Function in Cervical Spinal Cord Injury
Bibliographic record
Abstract
BACKGROUND: Upper limb function plays a significant role in enhancing independence for individuals with tetraplegia. However, there is limited knowledge about the specific input of sensorimotor deficits on upper limb function. Thus the theoretical framework designed to develop the Graded Redefined Assessment of Strength Sensibility and Prehension (GRASSP) was used as a hypothetical model to analyze the impact of impairment on function. OBJECTIVE: To define the association of impairment (sensation, strength, and prehension measured by the GRASSP) to upper limb function as defined by functional measures (Capabilities of Upper Extremity Questionnaire, Spinal Cord Independence Measure). METHODS: A hypothetical model representing relationships by applying structural equation modeling was used to estimate the effect of the impairment domains in GRASSP on upper limb function. Data collected on 72 chronic individuals with tetraplegia was used to test the hypothetical model. RESULTS: Structural equation modeling confirmed strong associations between sensation, strength, and prehension with upper limb function, and determined 72% of the variance in "sensorimotor upper limb function" was explained by the model. Statistics of fit showed the data did fit the hypothesized model. Sensation and strength influence upper limb function directly and indirectly with prehension as the mediator. CONCLUSIONS: The GRASSP is a sensitive diagnostic tool in distinguishing the relative contribution of strength, sensation and prehension to function. Thus, the impact of interventions on specific domains of impairment and related contribution on clinical recovery of the upper limb can be detailed to optimize rehabilitation programs.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".