Effect of Isometric Resistance Training and Kinesio-Taping on Rotator Cuff Muscle Injuries Among Club-Level Fast Bowlers
Bibliographic record
Abstract
Background: Background: Rotator cuff injuries are common among club-level fast bowlers, leading to pain, weakness, and reduced performance. Effective rehabilitation strategies are crucial for enhancing recovery and preventing re-injury.Objective: This study aimed to evaluate the effectiveness of isometric resistance training (IRT) combined with Kinesio-Taping (KT) in managing grade II rotator cuff muscle injuries among club-level fast bowlers.Methods: A total of 20 male fast bowlers with grade II rotator cuff injuries were randomly assigned to either a control group (n=10) or an experimental group (n=10). The experimental group underwent an 8-week rehabilitation protocol combining IRT and KT, while the control group received no specific intervention. Pre- and post-intervention assessments included the Western Ontario Rotator Cuff (WORC) questionnaire, physiotherapeutic tests, range of motion (ROM) measurements using a digital goniometer, and muscle strength evaluations through manual muscle testing. Data were analyzed using paired sample t-tests via SPSS version 25.Results: The experimental group showed significant improvements in ROM (mean difference = -0.850, p=0.000) and muscle strength across all physiotherapeutic tests (e.g., Lift-Off Test, mean difference = 1.320, p=0.000), compared to the control group.Conclusion: The combined use of IRT and KT significantly improved shoulder function, ROM, and muscle strength in fast bowlers with rotator cuff injuries. These findings support the inclusion of these interventions in sports rehabilitation protocols.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".