Effectiveness of a high-intensity laser for improving hemiplegic shoulder dysfunction: a randomized controlled trial
Bibliographic record
Abstract
Hemiplegic shoulder pain (HSP) is a common complication that occurs after stroke and has been reported in up to 84% of hemiplegic patients. One of the recommended treatment options for shoulder pain is high-intensity laser therapy (HILT). This study aimed to determine the effectiveness of high-intensity laser therapy on pain, function and hand grip strength in patients with hemiplegic shoulder dysfunction. Forty-four hemiplegic patients were randomly divided into two groups: Group 1 (study group, n = 22) received 3 HILT sessions a week for three weeks in combination with three sessions of therapeutic exercise per week for three weeks, and Group 2 (control group, n = 22) received a conventional exercise program for HSP three times a week for three weeks. Shoulder pain was evaluated using the McGill pain questionnaire (MPQ), the functional outcome of the shoulder was evaluated with the University of California-Los Angeles functional scale (UCLA), and handgrip strength was evaluated with a hydraulic hand dynamometer. The increase in the UCLA scores and the decrease in the MPQ scores after treatment were significant in the study group (p < 0.001) as well as in the control group (p < 0.05) in comparison with the pretreatment between-group comparison. Additionally, the increase in hand grip strength was significant in both groups after treatment (p < 0.001). The study group showed significant improvement over the control group with respect to the UCLA score, handgrip strength, and MPQ score (p < 0.001). HILT combined with therapeutic exercise provides greater improvement than therapeutic exercise alone in terms of hemiplegic shoulder pain, dysfunction, and handgrip strength.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".