Postural Sway Characteristics Are Affected by Alzheimer’s Disease
Bibliographic record
Abstract
The vestibular system, responsible for balance, is affected by Alzheimer's disease (AD). In this paper, linear and non-linear balance features were used to assess the postural stability of 13 AD individuals at mild stages in comparison with 16 healthy controls. Utilizing two accelerometers, the anterior-posterior (AP) and medial-lateral (ML) sways were recorded from the T2 vertebrae and lateral malleolus of participants standing on a solid and soft foam surface under both eyes-open and eyes-closed conditions. From the recorded signals, four features were extracted and used for statistical analysis: Number of Position Changes (NPC), Number of Non-Zero Accelerations (NNZA), Katz, and Higuchi fractal dimensions (KFD and HFD, respectively). The results show: 1) postural stability is significantly worse for the eyes-closed compared to eyes-open condition (P<0.05 for all features except HFD) as well as whilst standing on soft foam compared to the solid surface (P<0.05 for all features) in both groups; 2) balance perturbations were larger for AP sway than ML on both solid and foam surfaces in both groups (P<0.05 for NPC and NNZA); and 3) stationary balance is significantly poorer for AD individuals compared to controls (P<0.05 for all features). These observations show that both linear and non-linear characteristics of postural stability data have the potentials to be used as objective diagnostic aids for the detection of AD.
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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".