Changes in Strength, Sensation, and Prehension in Acute Cervical Spinal Cord Injury
Bibliographic record
Abstract
OBJECTIVE: To investigate the internal and external responsiveness and recovery profiles of the Graded Redefined Assessment of Strength, Sensibility, and Prehension (GRASSP) instrument in revealing changes in upper limb function within the first year following cervical spinal cord injury (SCI). METHOD: A European prospective, longitudinal, multicenter study assessing the GRASSP at 1, 3, 6, and 12 months after cervical SCI. Subtests of GRASSP were compared to the upper extremity motor (UEMS) and light touch scores (LT) according to the International Standards of Neurological Classification of Spinal Cord Injury (ISNCSCI), the Spinal Cord Independence Measure self-care subscore (SCIM-SS), as well as a clinician-rated outcome measure (CROM) of clinical relevance. Data were analyzed for GRASSP responsiveness and recovery rate over time. RESULTS: Seventy-four participants entered the study. GRASSP subtests proved responsive (standardized response mean [SRM] ranged from 0.79 to 1.48 for strength, 0.50 to 1.03 for prehension, and 0.14 to 0.64 for sensation) between all examination time points. In comparison, UEMS and LT showed lower responsiveness (SRM UEMS ranged from 0.69 to 1.29 and SRM LT ranged from 0.30 to -0.13). All GRASSP subtests revealed significant, moderate-to-excellent correlations with UEMS, LT, and SCIM-SS at each time point, and changes in GRASSP subtests were in accordance with the CROM. GRASSP prehension and motor recovery was largest between 1 and 3 months. CONCLUSION: The GRASSP showed excellent responsiveness, detecting distinct changes in strength and prehension relating to the severity of cervical SCI. It detected clinically significant changes complimentary to the ISNCSCI and SCIM-SS assessments.
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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 | 0.006 |
| 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 | 0.001 | 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".