<b>Prevalence of Rotator Cuff Tears in Adult Gym Trainers</b>
Bibliographic record
Abstract
Background: Gym trainers are occupationally exposed to high-frequency, high-intensity shoulder movements, predisposing them to rotator cuff injuries. Despite the physical demands of their role, there is limited epidemiological data specifically quantifying the burden of rotator cuff pathology in this professional group. Objective: To determine the prevalence and severity of rotator cuff tears and associated shoulder pain among adult gym trainers in Lahore, and to examine their relationship with occupational exposure. Methods: A cross-sectional observational study was conducted among 164 certified gym trainers aged 22–40 years using non-probability convenience sampling. Data were collected using the Western Ontario Rotator Cuff Index (WORC) and analysed with SPSS version 27. Descriptive statistics summarized demographic variables, and chi-square tests and odds ratios assessed associations between rotator cuff severity, pain intensity, gender, and years of experience. A p-value <0.05 was considered statistically significant. Results: Among participants, 80.5% had rotator cuff tears and 75.6% reported shoulder pain. Tear severity correlated significantly with pain intensity (χ²=24.32, p<0.001), with severe tears associated with 19.2-fold higher odds of severe pain. WORC scores increased with years of experience, suggesting cumulative exposure as a contributing factor. No significant differences were observed by gender or age group. Conclusion: Rotator cuff pathology and shoulder pain are highly prevalent among gym trainers, with strong evidence of a dose-response relationship to occupational exposure. Targeted preventive strategies are urgently needed to mitigate long-term musculoskeletal disability in this workforce.
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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 | 0.001 | 0.001 |
| 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) | 0.003 | 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".