Impairment assessment of lateral epicondylitis through electromyography and dynamometry.
Bibliographic record
Abstract
OBJECTIVES: To investigate changes in muscular activity and strength of subjects diagnosed with lateral epicondylitis (LE). To assess the appropriateness of these measures in the patient's follow-up. METHODS: Twenty-four subjects (11 men and 13 women) with LE, were evaluated at baseline and after 5 weeks of an experimental treatment. Measurements included: the (1) pain-free grip (PFG), (2) maximal isometric strength, (3) surface electromyography (EMG) of forearm muscle (healthy and affected), (4) a visual analogue scale (VAS), and (5) the Patient Rated Tennis Elbow Evaluation (PRTEE) (Canadian-French version). RESULTS: All subjects showed improvement in VAS and PRTEE. The maximal isometric strength during flexion and extension of the wrist and the EMG analysis failed to discriminate the affected from the healthy elbow during the initial assessment. Only the PFG measured with the elbow in extension could discriminate elbows with LE from the healthy ones. CONCLUSIONS: The use of the PFG with the elbow in extension seems to be the most indicated strength measurement to monitor the recovery of patients with LE. The EMG acquisition protocol used in this research was not adequate to monitor effectively the recovery of LE.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".