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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".