Low-Back Pain and Knee Position–Related Differences on Postural Control Measures During a One-Legged Stance in Athletes
Bibliographic record
Abstract
CONTEXT: Chronic low-back pain (CLBP) may be associated with changes in postural balance in athletes as poor postural control during sports practice. OBJECTIVE: To compare the postural control of athletes with and without CLBP during 2 one-legged stance tasks and identify the center of pressure (COP) cutoff values to determine the main differences. Designed: A cross-sectional study. SETTING: Laboratory of functional evaluation and human motor performance. PARTICIPANTS: A total of 56 male athletes, 28 with and 28 without CLBP (mean age = 26 y). INTERVENTION: The one-legged stance with knee extension and with the knee at 30° flexion tasks were measured and analyzed on a force platform. The participants completed three 30-second trials (30 s of rest between each trial). MAIN OUTCOME MEASURES: The COP parameters: the area of COP, mean COP sway velocity in both the anteroposterior and mediolateral directions, and total COP displacement were computed, and a receiver operating characteristics curve analysis was applied to determine the group differences. RESULTS: Athletes with CLBP had poorer postural control (P < .01) in both tasks. The 30° knee flexion reported more postural instability than the knee extension for all COP parameters (a large effect size d = 0.80).The knee extension cutoffs identified were >7.1 cm2 for the COP area, >2.6 cm/s for the COP sway velocity in the anterior-posterior direction, and >3.2 cm/s for the mediolateral direction. Whereas, the 30° knee flexion cutoffs were >10.9 cm2 for the COP area, >2.9 cm/s for the COP sway velocity in the anterior-posterior direction, and >4.1 cm/s for the mediolateral direction. Both measures showed enough sensitivity and specificity (ie, area under the curve = 0.88 in and 0.80, respectively) to discriminate both groups. CONCLUSIONS: The athletes with CLBP had poorer postural control than the healthy athletes and obtained specific cutoff scores from the COP values.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 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".