Responsiveness, Sensitivity, and Minimally Detectable Difference of the Graded and Redefined Assessment of Strength, Sensibility, and Prehension, Version 1.0
Bibliographic record
Abstract
As spinal cord injury (SCI) trials begin to involve subjects with acute cervical SCI, establishing the property of an upper limb outcome measure to detect change over time is critical for its usefulness in clinical trials. The objectives of this study were to define responsiveness, sensitivity, and minimally detectable difference (MDD) of the Graded Redefined Assessment of Strength, Sensibility, and Prehension (GRASSP). An observational, longitudinal study was conducted. International Standards of Neurological Classification of SCI (ISNCSCI), GRASSP, Capabilities of Upper Extremity Questionnaire (CUE-Q), and Spinal Cord Independence Measure (SCIM) were administered 0-10 days, 1, 3, 6, and 12 months post-injury. Standardized Response Means (SRM) for GRASSP and ISNCSCI measures were calculated. Longitudinal construct validity was calculated using Pearson correlation coefficients. Smallest real difference for all subtests was calculated to define the MDD values for all GRASSP subtests. Longitudinal construct validity demonstrated GRASSP and all external measures to be responsive to neurological change for 1 year post-injury. SRM values for the GRASSP subtests ranged from 0.25 to 0.85 units greater than that for ISNCSCI strength and sensation, SCIM-SS, and CUE-Q. MDD values for GRASSP subtests ranged from 2-5 points. GRASSP demonstrates good responsiveness and excellent sensitivity that is superior to ISNCSCI and SCIM III. MDD values are useful in the evaluation of interventions in both clinical and research settings. The responsiveness and sensitivity of GRASSP make it a valuable condition-specific measure in tetraplegia, where changes in upper limb neurological and functional outcomes are essential for evaluating the efficacy of interventions.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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".