Pregnant and non-pregnant women and low back pain-related differences on postural control measures during different balance tasks
Bibliographic record
Abstract
Introduction: Low back pain (LBP) is the most common musculoskeletal complaint in pregnancy, being responsible for many negative impacts. Objective: To evaluate the effect of LBP on static and dynamic balance in pregnant women and whether pregnancy mediates the results compared to non-pregnant women. Methods: 44 women (mean age 30 yrs) participated voluntarily in this study: 16 pregnant women with LBP starting in pregnancy, 14 pregnant women without LBP and 14 non-pregnant women as a group control. Participants were assessed for static postural balance using a force platform and dynamic mobility balance using the Timed Up and Go (TUG) test. Results: The pregnant women with LBP showed significant (P < 0.04, for mean, d= 1,2) poor postural balance in static tests (force platform), in the area of COP eyes open. In dynamic balance (TUG test), statistical difference was found between the groups (P 0.038) and the effect size were moderate to strong in the comparison between the three groups. The most sensitive differences were reported mainly between pregnant women with LBP versus non-pregnant control group in balance measures from force platform. Conclusion: The findings indicate that LBP associated to pregnant clinical status can decrease the balance capacity in women. These results have implication for balance evaluation and retraining in pregnant women with and without LBP from rehabilitation or prevention programs.
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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 | 0.003 |
| 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) | 0.002 | 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".