Integration of Core Strengthening and Respiratory Techniques in Physiotherapy for Recreational Weightlifters: A Case Study
Bibliographic record
Abstract
Background: Core weakness and improper breathing patterns are common risk factors for Weightlifting injuries. By incorporating targeted exercises and breathing techniques into Physiotherapy programs, practitioners can help mitigate these risks and promote long-term musculoskeletal health among weightlifters. The aim of the study is to investigate the effect of Integrating Core Strengthening and Respiratory Techniques in Physiotherapy for Weightlifters. Methodology: The subjects were given a combination of four routines, namely crunches, rotating crunches, supine leg and arm raise, prone bridge. The training protocols were done twice every week for 30 Minutes for a period of 4 weeks; all the exercises were performed immediately after a standardized 10-minute warm-up that included jumping jacks, lunges, shoulder and trunk rotation Results: Following the intervention, improvements were observed in muscular endurance across various torso muscle groups, as indicated by increased performance in plank tests and McGill’s torso muscular endurance test battery. Muscle balance ratios also showed favourable adjustments post-intervention. Conclusion: The study concludes that a combination of four exercises-crunches, crunches with rotation, supine leg and arm raises, and prone bridging-alongside simultaneous deep breathing aids weightlifters in enhancing their core strength. Key words: Core Strengthening, weightlifters, breathing exercises, McGill's torso muscular endurance test battery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| 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".