The Graded and Redefined Assessment of Strength, Sensibility, and Prehension Version 2 Provides Interval Measure Properties
Bibliographic record
Abstract
The Graded and Redefined Assessment of Strength, Sensibility and Prehension (GRASSP) is a valid, reliable, and responsive outcome measure to evaluate upper limb function in individuals with tetraplegia. GRASSP generates ordinal total scores; therefore, applicability as an interval level measurement requires testing of its measurement properties. This study examined the metric characteristics with Rasch Analysis to derive interval level scales of the respective GRASSP subtests. The GRASSP was recorded within 10 days, and at 1, 3, 6, and 12 months after cervical spinal cord injury (SCI). Rasch analysis was performed for each GRASSP subscale to assess the following metric assumptions: absence of local item dependency (LID), unidimensionality, monotonicity, item and model fit, reliability, and absence of differential item functioning (DIF) for side (left and right) and examination stage. If these assumptions could not be met, adjustments were undertaken to achieve a good fit to the Rasch model. Seventy-seven individuals with cervical SCI were included (n = 154 arms). Stacking the data for the side (left and right) resulted in a total of 614 observations, which were based on the repeated measurements. With minor adjustments, the GRASSP subscales showed good reliability, item fit, and ordered response options. Local item dependencies were found in the strength and sensibility subscales. Redundancies among some measurement items allowed shortening of the subscales without reasonable loss of reliability. Absence of DIF for the examination stage supported robustness of the subscales over time. The modified GRASSP, now Version 2, subtest scores can be applied as interval level measurements, and the reduction of items within subscales allows for shorter assessment times in clinical studies without degrading metric properties.
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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 | 0.003 | 0.012 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".