Does cognitive performance explain the gap between physiological and perceived fall-risk in people with multiple sclerosis?
Bibliographic record
Abstract
Background Cognitive impairment is linked with increased risk of falls in people with multiple sclerosis (pwMS), but it is not clear whether cognitive performance may help to account for the discordance between fall-risk due to actual physiological functioning and the individual's perception of their fall-risk. This study examined the relationship between cognitive performance and the concordance/discordance of physiological and perceived fall-risk in pwMS. Methods : In this single-center cross-sectional analysis of 201 pwMS, proxies for physiological (gait speed) and perceived (Modified Falls Efficacy Scale) fall-risk were collected. Participants were categorized into 4 groups using established cut-off values: high physiological – high perceived (H phy -H per ), high physiological – low perceived (H phy -L per ), low physiological – low perceived (L phy -L per ), and low physiological – high perceived (L phy -H per ) fall-risk. Cognitive performance was evaluated using the NeuroTrax computerized cognitive battery, which generates a global cognitive score (GCS) as well as scores for individual cognitive domains. Results Overall, 27.4 % of participants exhibited a discordance between physiological and perceived fall-risk. Individuals with discordant fall-risk did not have worse cognitive scores than individuals with concordant fall-risk, whether GCS or individual cognitive domains. However, among concordant groups, participants in the H phy -H per group had worse cognitive scores (GCS) as well as information processing, attention, motor skills, executive function and visual spatial domain scores than participants in the L phy -L per group. Conclusion In this study, one in 4 pwMS had a discordance between their physiological and perceived fall-risk. This discordance was not explained by cognitive performance.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".