The effect of a selected spinal core-muscle stabilization training in water on pain intensity and lumbar lordosis
Bibliographic record
Abstract
Background: In recent years, a special emphasis has been placed on the abdominal transverse and multifidus muscles for the treatment of back pain and lumbar lordosis. Evidence reveals the difference in the function of these muscles among the individuals. This study aimed to examine the effect of a selected spinal core-muscle stabilization training in water on pain intensity and lumbar lordosis of female college students. Materials and Methods: This quasi-experimental study was performed on 43 volunteer students (22 and 21 in each of the experimental and control groups, respectively; mean age 18-25) with the history of low back pain. The experimental groups performed a 12-week hydrotherapy exercise (3 sessions per week) under the supervision of researcher and the control group did not perform any muscle training. Data were collected using a Quebec questionnaire and a flexible ruler on the first and last days of training for each participant. Results: Results showed a significant decrease in back pain intensity and lumbar lordosis angle (P≤0.0001) in the experimental group, while no significant difference was observed between pretest and posttest data in the control group. Conclusion: Findings reveal that the core-muscle stabilization training of the spinal cord in water decreases the pain intensity and lumbar lordosis angle.
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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 | 0.000 |
| 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) | 0.003 | 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".