Perturbed walking balance recovery between premenopausal and postmenopausal females
Bibliographic record
Abstract
BACKGROUND: Early postmenopausal females are at increased risk for falls and fragility fractures, despite often not meeting the diagnostic criteria for osteoporosis or showing noticeable declines in gait and balance. METHODS: Fifteen premenopausal females (aged 35-53) and fifteen postmenopausal females (aged 50-66) participated in this study. The Gait Real-time Analysis Interactive Laboratory was utilized to simulate unexpected perturbations during walking. Variables comprising the number of contralateral limb steps, step length, width, height, stance time, and double support time were analysed and compared between the groups in both natural and perturbed gait using statistical parametric mapping. FINDINGS: During the swing phase of the contralateral, unperturbed limb, most premenopausal females completed the swing phase without prematurely lowering their legs and taking additional steps with it. Conversely, most postmenopausal participants lowered their contralateral, unperturbed leg and took one or two steps with it before fully entering the swing phase and proceeding to the heel strike of the next gait cycle. Moreover, a smaller step width and shorter step height were observed during the perturbed gait cycle in the postmenopausal group compared to the premenopausal group. INTERPRETATION: Menopausal status significantly impacted balance recovery strategies and gait spatial parameters during balance-challenging perturbed conditions. These findings highlight the importance of applying perturbations and using spatial parametric mapping in further research to better understand gait stability and its role in fall risk during early menopause.
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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 | 0.001 |
| 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) | 0.003 | 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".