The Relationships Between Some Physical Fitness Factors and Muscle Damage in People With Intellectual Disabilities
Bibliographic record
Abstract
Objectives: The physical component of tasks can be a problem because of muscle weakness; therefore, this research was performed to investigate the relationships between some physical fitness factors and muscle damage enzyme levels in people with mild Intellectual Disabilities (ID). Methods: In this cross-sectional study, 314 students with ID (Body Mass Index [BMI]: 23.42±4.84 kg/m2), after measuring their anthropometric variables (height, weight, and BMI), performed the Canadian Standardized Test of Fitness (CSTF). This test includes flexibility (sit and reach test), muscle endurance (Push-ups and Sit-ups tests), muscle strength (Hand-grip test), and cardiorespiratory endurance (Canada Home Fitness test). Then saliva samples were collected from 20 subjects who were homogeneous for age (Mean±SD: 20.30±2.27 years), sex (male only), and type of ID, for measuring their saliva levels of Creatine Phosphate Kinase (CPK), Lactate Dehydrogenase (LDH), and C-reactive Protein (CRP). Data were analyzed by the Pearson correlation coefficient and linear and nonlinear regressions (P<0.05). Results: The findings indicated significant relationships between some of the physical fitness factors and some enzymes of muscle damage (P<0.05). The results indicated significant relationships between CPK, with push-ups, sit-ups, and hand-grip strength, as well as between the LDH with push-ups and CRP with sit-ups and hand-grip strength (P<0.05). Discussion: According to the results of the present research, CPK, LDH, and CRP saliva levels in males with an ID have a significant relationship with some of the physical fitness components. Also, the results of the present research have provided equations for estimating CPK, LDH, and CRP saliva levels through CSTF battery test scores in people who have an educable ID.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.001 | 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".