Effects of abdominal bracing maneuver during walking on trunk muscle endurance in healthy staff of Northwest Institute: A randomized controlled pilot study
Bibliographic record
Abstract
Objectives: This study aimed to find the effect of abdominal bracing (AB) maneuver during walking on trunk muscle endurance in healthy individuals. Methods: A randomized control pilot study was conducted at Northwest Institute of Health Sciences Peshawar from July to December 2023. A total of 32 participants aged 25–40 were randomized to Groups A and B. Group A received AB maneuver with walking, while Group B received walking only. Both groups received a total of 12 sessions. Pre- and post-endurance testing was performed using McGill’s Torso Muscular Endurance Test. Results: The mean age of participants in Group A was 29.06 ± 5.14 years, while the mean age of participants in Group B was 27.62 ± 2.84 years. The mean and standard deviation of the flexor endurance test between Groups A and B after treatment were 27.37 ± 3.26 and 16.21 ± 2.24, respectively. A significant difference was observed in flexor endurance and right and left lateral flexor endurance between the two groups (P < 0.05). In contrast, no significant difference was observed in extensor endurance between the two groups, having P > 0.05. Within-group analysis in Group A revealed a significant difference in pre-post-intervention flexion, right and left lateral flexors, and extensors (P < 0.05). However, within-group analysis in Group B revealed a statistical difference in flexors endurance, which is not clinically significant. Conclusion: The AB maneuver with walking compared with walking alone may improve the endurance of the trunk flexors, specifically the right and left lateral flexors.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".