Physiological, Sensory, and Functional Measures in a Model of Wrist Muscle Injury and Recovery
Bibliographic record
Abstract
PURPOSE: To evaluate the effectiveness of muscle rehabilitation modalities, it is first necessary to develop a model to test measures that would assess physiological, sensory, and functional muscle recovery. This study attempted to develop such a model for wrist injury. SUBJECTS: Healthy male and female adults (n = 25). METHODS: SUBJECTS performed wrist muscle damage assessment, soreness, discomfort, difficulty, and functional motor task tests before and 1, 2, and 7 days after eccentric wrist muscle contractions. Wrist-related motor task tests, including the perception of discomfort and difficulty during performance, were also conducted. RESULTS: At 24 hours post-eccentric exercises, wrist extension and flexion force declined (p < 0.05) and soreness (p < 0.05) and circumference (p < 0.05) increased; all returned to normal by 7 days post-exercise. At 24 and 48 hours post-exercise, perception of discomfort and difficulty was elevated during performance of motor tasks (p < 0.05). The completion speed of motor tasks was unaffected at any time post-eccentric exercise (p > 0.05). CONCLUSIONS: Loss of wrist muscle force, increased soreness, task discomfort, and difficulty were noted following eccentric exercise. However, subjects appeared able to compensate, such that the speed of completion of motor tasks was not slowed. Longer or more specific motor tasks may be necessary to mimic real work performance decrement and recovery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".