The Graded Redfined Assessment of Strength, Senssibility and Prehension (GRASSP): Development of the Scoring Approach, Evaluation of Psychometric Properties and the Relationship of Upper Limb Impairment to Function
Bibliographic record
Abstract
Upper limb function is important for individuals with tetraplegia because upper limb function supports global function for these individuals. As a result, a great deal of time and effort has been devoted to the restoration of upper limb function. Appropriate outcome measures that can be used to characterize the neurological status of the upper limb have been one of the current barriers in substantiating the efficacy of interventions. Techniques and protocols to evaluate changes in upper limb neurological status have not been applied to the SCI population adequately. The objectives of this thesis were to develop a measure; which is called the Graded Redefined Assessment of Strength Sensibility and Prehension (GRASSP). Development of the scoring approach, testing for reliability and construct validity, and determining impairment and function relationships specific to the upper limb neurological were established. The GRASSP is a clinical measure of upper limb impairment which incorporates the construct of “sensorimotor upper limb function”; comprised of three domains which include five subtests. The GRASSP was designed to capture information on upper limb neurological impairment for individuals with tetraplegia. The GRASSP defines neurological status with numerical values, which represent the deficits in a predictive pattern, is reliable and valid as an assessment technique, and the scores can be used to determine relationships between impairment and functional capability of the upper limb. The GRASSP is recommended for use in the very early acute phases after injury to approximately one year post injury. Use of the GRASSP is recommended when a change in neurological status is being assessed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".