Relationships Between Functional Movement Screen and Pain, Dynamic Balance, and Trunk Muscle Endurance in Military Personnel With Non-specific Chronic Low Back Pain
Bibliographic record
Abstract
Purpose: Functional disability, impaired balance, and trunk muscle endurance are among the major changes in patients with Non-specific Chronic Low Back Pain (NCLBP). Investigating the relationship between these factors and Functional Movement Screen (FMS) can facilitate effective pain management and functional problems in these patients. This study aimed to assess the relationships between FMS and pain, dynamic balance, and trunk muscle endurance in military personnel with NCLBP. Methods: The present study was of a correlational research design. The study subjects were 50 male military personnel with NCLBP (Mean±SD age=33.30±3.94 y, height= 175.32±5.50 cm, & weight=74.05±3.64 kg). FMS was evaluated by FMS tests and pain severity was assessed through Quebec Back Pain Disability Scale; the dynamic balance was evaluated by Y-Balance Test (YBT), and the trunk muscle endurance was measured by the ITO test. Statistical analysis was performed by SPSS. Pearson correlation coefficient at a significance level of P<0.05 was used to examine the association between the research variables. Results: Pain (P=0.04, r=-0.285) was negatively correlated with the FMS. The FMS was positively associated with the dynamic balance (P=0.014, r=0.346) and trunk muscle endurance (P=0.02, r=0.381). Conclusion: The FMS can be recommended as a functional assessment tools to identify functional deficits in military personnel with NCLBP. The data suggested that the researchers could employ the FMS as a useful tool in designing more effective treatment plans and improving the functional capacity of individuals with CLBP.
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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 | 0.002 |
| 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) | 0.003 | 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".