The Effects of Consecutive Supervised Functional Lumbar Stabilizing Exercises on the Postural Balance and Functional Disability in Low Back Pain
Bibliographic record
Abstract
Objectives: The aim of this study was to examine the effects of consecutively supervised core stability training on postural control and functional disability in female patients with non-specific chronic low back pain. Methods: Twenty nine female participants with non-specific chronic low back pain participated in the study. They were randomly divided into two groups: experimental group (10 days consecutively core stability exercises under physical therapist’s supervision) and control group (without intervention). Before and after the intervention, stability situations, pain intensity and functional disability were assessed with Biodex, visual Analogue Scale, Oswestry and Quebec questionnaire scales respectively. Data were analyzed by using statistical methods, independent T test and ANCOVA. Results: The study results indicated no statistically significant differences in all variables except age between two groups before intervention. Analysis by ANCOVA showed a significant difference in disability, pain intensity, Overall Stability Index with Double Leg Eyes Closed, Anterior-Posterior Stability Index with Double Leg Eyes Closed and Medio-Lateral Stability Index with Double Leg Eyes Closed scores between two groups after intervention. However, other variable differences were not significant while these changes were greater in the intervention group. Discussion: The present study indicates that consecutively supervised core stability training is an effective approach in pain relief and improving postural control in female patients with non-specific chronic 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 | 0.000 | 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".