Interrelation between functional decline and dementia: The potential role of balance assessment
Bibliographic record
Abstract
OBJECTIVE: There has been growing interest in the past few years on the relationship between impairment of motor functions and cognitive decline, so that the first can be considered a marker of dementia. In MCI patients, the deficit in processing visual information interferes with postural control, causing oscillations and instability. Postural control is usually evaluated through the Short Physical Performance Battery (SPPB) test or Tinetti scale, but, to our knowledge, there are no many studies that considered the Biodex Balance System (BBS) in the evaluation of postural controls in MCI patients. The aim of this study was first to confirm the bi- directional relationship between cognitive and motor performance, and then to compare traditional evaluation scales (SPPB and Tinetti) with a biomechanical tool, the BBS. MATERIALS AND METHODS: Observational retrospective study. In 45 elderly patients with cognitive impairment we evaluated cognition, assessed with the MMSE and MoCA, malnutrition with the MNA, and sarcopenia with DEXA (ASMMI). Motor performance was assessed with SPPB, Tinetti, and BBS. RESULTS: MMSE correlated more with BBS than with the traditional scales, while MoCA was also correlated with SPPB and Tinetti scores. CONCLUSIONS: BBS had a stronger correlation with cognitive performance compared with the traditional scales. The relationship between MoCA executive items and the BBS tests suggests the usefulness of targeted interventions involving cognitive stimulation to improve motor performance, and motor training to slow the progression of cognitive decline, particularly in MCI.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".