Characteristics of Upper Limb Impairment Related to Degenerative Cervical Myelopathy: Development of a Sensitive Hand Assessment (Graded Redefined Assessment of Strength, Sensibility, and Prehension Version Myelopathy)
Bibliographic record
Abstract
BACKGROUND: Degenerative cervical myelopathy (DCM) involves spinal cord compression, which causes neurological decline. Neurological impairment in DCM is variable and can involve complex upper limb dysfunction including loss of manual dexterity, hyperreflexia, focal weakness, and sensory impairment. The modified Japanese Orthopaedic Association (mJOA) score relies on the patients' subjective perceptions, whereas existing objective measures such as strength and sensory testing do not capture subtle changes in dexterity and function. OBJECTIVE: 1) To characterize arm and hand function in DCM; and 2) To develop and validate Graded Redefined Assessment of Strength, Sensibility, and Prehension Version-Myelopathy (GRASSP-M), a clinical assessment that quantifies upper limb impairment. METHODS: A total of 148 DCM patients (categorized into mild, moderate, and severe based on mJOA grade) and 21 healthy subjects were enrolled. A complete neurological exam, the mJOA, the QuickDASH, grip dynamometry, and the GRASSP-M were administered. RESULTS: Strength, sensation, and manual dexterity significantly declined with increasing DCM severity (P ≤ .05). Impairment in hand dexterity showed better discrimination between mild, moderate, and severe DCM categories than strength or sensation. The GRASSP-M was found to be both a reliable (intraclass correlation coefficient >0.75 for intra- and inter-rater reliability) and valid (with both concurrent and construct validity) tool. CONCLUSION: These results demonstrate that patients' subjective reporting of functional status, especially in the mild DCM category, may underrepresent the extent of functional impairment. The GRASSP-M is an objective tool designed to characterize patients' functional impairment related to the upper limb, which proves useful to diagnose and quantify mild dysfunction, monitor patients for deterioration, and help determine when patients should be treated surgically.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".