Spatial orientation and temporal analysis of computerized dynamic posturography for balance assessment in non-complaining elderly
Bibliographic record
Abstract
Abstract Background The global population of 60 and over is rapidly increasing. The aging process seriously impacts postural control, resulting in decreased balance performance and a heightened risk of falls. This work aimed to study geriatrics’ static and dynamic balance using spatial and temporal posturography analysis. Methods The current study was an observational cross-sectional study on 60 subjects aged > 61 years and 40 subjects aged ≤ 60 years who were relatives of outpatient clinic patients. Studied individuals were subjected to clinical balance assessment and computerized dynamic posturography (CDP). Results The age of included geriatrics was 61–79 years, and 88.3% were males. There was a significant decline in Advanced Activities of Daily Living scale (AADLs), Montreal Cognitive Assessment scale (MoCA), and the Dynamic Gait Index (DGI) scales in older adults. There was a substantial decrease in CDP somatosensory, vision, and vestibular ratios in the older age group with p < 0.001. There was a reduction in the stability rate platform and cancellation time and an increase in spectral power index in the elderly. There was no correlation between CDP visual, vestibular, and somatosensory functions and age, AADL, MoCA, and DGI single and dual-tasks scales. Conclusion There was a decrease in the stability rate platform and cancellation time and an increase in the spectral power that reflects the dynamic aspects of balance. CDP visual, vestibular, and somatosensory functions did not correlate with age, AADL, MoCA, DGI single- and dual-tasks scales, and medications.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".