Investigating postural control as a predictor of low back and pelvic girdle pain during and after pregnancy
Bibliographic record
Abstract
BACKGROUND: Falls are common during pregnancy, posing risks to maternal and fetal health. Pregnant individuals also commonly experience low back and/or pelvic girdle pain. Other populations with pain, such as older adults with back pain demonstrate increased fall risk. This study assessed the relationship between standing balance control characteristics during single leg stance and low back/pelvic girdle pain scores during and after pregnancy with eyes open and closed. We hypothesized that standing balance control characteristics of smaller sway and sway velocity would be related to greater low back/pelvic girdle pain during the third trimester. METHODS: During the second trimester, third trimester, and postpartum nineteen individuals performed single leg stance on a force platform with eyes open and closed and completed the Quebec Back Pain Disability Scale. Stepwise multiple linear regressions were used to investigate the variance of Quebec Back Pain Disability Scale scores that could be explained by postural control variables for each time point and condition. FINDINGS: = 0.480). No significant relationships were found during the second trimester or postpartum nor for eyes open conditions. INTERPRETATION: This study suggests a potential association between low back/pelvic girdle pain and postural control during pregnancy. Pregnant individuals with lumbopelvic pain may demonstrate postural stability deficits, particularly without visual input. Balance assessments and interventions should be considered during routine care for pregnant individuals, especially for those with pain.
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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".