Evaluating Grasp Function in Patients With Chronic Inflammatory Demyelinating Polyneuropathy Using Dynamometers: A Comprehensive Review
Bibliographic record
Abstract
Chronic inflammatory demyelinating polyneuropathy (CIDP) is a progressive neurological disorder characterized by weakness and impaired sensory function due to damage to peripheral nerves. Evaluating grasp function is critical for understanding the impact of CIDP on patients' daily activities and guiding rehabilitation strategies. This comprehensive review examines the role of dynamometers in quantifying grip strength deficits, tracking disease progression, and assessing treatment outcomes in CIDP patients. Key findings highlight the utility of dynamometers in quantifying grip strength deficits, tracking disease progression, and evaluating treatment outcomes. The review also explores methodological considerations, such as standardizing testing protocols and integrating dynamometric measurements with clinical scales. By providing insights into the functional impairments associated with CIDP and the effectiveness of therapeutic interventions, this review underscores the role of dynamometry in advancing patient care and enhancing the quality of life for individuals living with this condition. Future research directions include the development of more sensitive dynamometric tools and longitudinal studies to better understand the relationship between grip strength and overall disease trajectory in CIDP.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".