Cognitive-Postural Interference in Multiple Sclerosis
Bibliographic record
Abstract
Multiple Sclerosis (MS) is a neurodegenerative disease associated with cognition and balance impairments, which can lead to accidental falls. Postural control requires cognitive resources. This interaction is quantifiable by using the dual-task paradigm. The cognitive-postural interference (CPI) is commonly evaluated through an assessment of the dual-task cost (DTC). The aim of this review was to summarize literature related to process, results and effects of CPI in MS patients. The Prisma statement was used to guide this systematic review. Eligible articles had to include participants with MS for whom CPI was assessed using the DTC. A total of 14 articles meeting inclusion criteria were retained. All studies used the double stance with eyes open for the postural task component. Three types of cognitive tasks were used: Stroop Color-Word Test (SCWT), Word List Generation and Backward Counting. However, cognitive task scores in single or dual task were unavailable in 11 studies, which prevented calculating the DTC for that task. Prioritization instructions were provided in seven studies. Mutual interference was shown in three studies, postural interference in nine and postural facilitation in two. This review highlights the presence of CPI among MS patients. Postural interference usually occurred during dual task while cognitive performance during dual task was rarely reported. Postural task performance does not appear to vary based on EDSS level. We advise authors of future studies to use the SCWT in combination with postural task measure (sway area and postural sway) for DT assessment, with explicit prioritization instructions. Further, the cognitive and postural tasks should be performed in ST and DT and all results should be presented.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".