Subclinical postural instability detected by stabiloplatform examination in the patients with vascular mild cognitive impairment--Part 1.
Bibliographic record
Abstract
INTRODUCTION: Postural instability and balance dysfunction have been identified in the patients with dementia. AIM: The aim of our study was to evaluate subclinical postural and balance features in patients with vascular mild cognitive impairment (VaMCI). METHODS: The study subjects were the patients with VaMCI (n = 62) and those without cognitive impairment (n = 35). Our cognitive performance examination consisted of the battery of tests including Luria memory words test, Shulte's tables, semantic and phonemic fluency test, the clock drawing test and Montreal cognitive assessment (MOCA). Postural function and balance control were assessed by computerized static ("Stabilan 01" Russia) and dynamic ("Gravistat", Belarus) stabiloplatforms using a biofeedback principle. RESULTS: In the static stabiloplatform examination more pronounced postural instability in VaMCI patients was evidenced by larger gravity center displacement radius (p < 0.05) and confidence ellipse area (p < 0.05). In the dynamic stabiloplatform examination it was manifested by the reduced number of errors as attempts to maintain postural stability (p < 0.0001), a decrease in the error rate (p < 0.0001) and increase in the average time of a postural response as time spent for one error (p < 0.0001). CONCLUSIONS: Subclinical postural instability detected by the stabiloplatform examinations may be of value in earlier VaMCI diagnosis.
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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 | 0.001 |
| 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".