The effect of neuromuscular training on hip strength, core and jump-landing mechanics in athletes with anterior cruciate ligament reconstruction
Bibliographic record
Abstract
Background and Aim: the high incidence re-injury of ligament reconstituted is one of the complex post-operative problems. Therefore, the purpose of this study was to investigate the effect of neuromuscular training on hip and core strength and jump-landing mechanics in athletes with anterior cruciate ligament reconstruction. Materials and Methods: Twenty-four athletes with a history of anterior cruciate ligament reconstruction were selected randomly and accessibility, and divided into experimental and control groups. Before and after the protocol of neuromuscular training (8 weeks, 3 sessions per week) from both groups, the isometric strength of hip, core muscles endurance and jump-landing mechanics were assessed using isokinetic dynamometer, McGill’s core stability tests and jump-landing error scoring system. Data were analyzed paired sample t-test and repeated measures anova significant level (P <0.05). Results: The results of intra group changes in the experimental group showed that there was a significant improvement in isometric muscles strength, core endurance, and jump-landing error score in post-test compared to pre-test (P <0.05) that these changes control group was not significant. Also, there was a significant difference between groups in both experimental and control groups in all variables (P <0.05). Conclusion: Based on these results, it can be concluded that the proximal parts of the knee have a significant effect on the motor control strategies.
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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) | 0.001 | 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".