Comparison of Core Muscles Endurance in Sited Occupied Staff and Non-sited Occupied Staff of Shiraz University of Medical Sciences
Bibliographic record
Abstract
Abstract Objective Core muscle dysfunction might serve as a risk factor for future musculoskeletal dysfunctions considering the high percentages of adults employed in mainly sedentary occupations in Iran, there is a need to clarify the strength of evidence on the potentially deleterious impact of prolonged sitting at work on the biomechanics of core/trunk muscles. This study aims to evaluate trunk/core muscle endurance in employees of seated and non-seated jobs in Shiraz University of Medical Sciences. Method A total number of 100 employees of Shiraz University of Medical Sciences (SUMS) were studied in 2 groups, seated jobs (n=50) and non-seated jobs (n=50). Seated jobs (office employees) defined as the jobs’ nature requiring the employees to sit more than half of their work time in a day, whereas non-seated jobs were those requiring less than half a workday to sit. Trunk endurance time measured by the 4 different stabilization tests including McGill’s trunk flexor endurance test, Sorenson’s trunk extensor endurance test and right and left trunk lateral flexor endurance test (Side-Bridge test). Results Statistical analysis was performed using the SPSS software version 21 (SPSS, Chicago, IL) Independent t test was used individuals in non-seated group had a statistically significant higher trunk endurance time for all 4 static tests (all P-values<0.001). Conclusion Prolonged occupational sitting is associated with reduced core muscle endurance. It may cause relationship between weakened core/trunk muscles and development of specific occupational musculoskeletal dysfunctions such as low back pain.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".