Role of executive functions in prospective memory in multiple sclerosis: Impact of the strength of cue–action association
Bibliographic record
Abstract
OBJECTIVES: Patients diagnosed with multiple sclerosis (MS) often report prospective memory (PM) deficits. Although PM is important for daily functioning, it is not formally assessed in clinical practice. The aim of this study was to examine the role of executive functions in MS patients' PM revealed by the effect of strength of cue-action association on PM performance. METHOD: Thirty-nine MS patients were compared to 18 healthy controls matched for age, gender, and education on a PM task modulating the strength of association between the cue and the intended action. RESULTS: Deficits in MS patients affecting both prospective and retrospective components of PM were confirmed using 2 × 2 × 2 mixed analyses of variance (ANOVAs). Among patients, multiple regression analyses revealed that the impairment was modulated by the efficiency of executive functions, whereas retrospective memory seemed to have little impact on PM performance, contrary to expectation. More specifically, results of 2 × 2 × 2 mixed-model analyses of covariance (ANCOVAs) showed that low-executive patients had more difficulty detecting and, especially, retrieving the appropriate action when the cue and the action were unrelated, whereas high-executive patients' performance seemed to be virtually unaffected by the cue-action association. CONCLUSIONS: Using an objective measure, these findings confirm the presence of PM deficits in MS. They also suggest that such deficits depend on executive functioning and can be reduced when automatic PM processes are engaged through semantic cue-action association. They underscore the importance of assessing PM in clinical settings through a cognitive evaluation and offer an interesting avenue for rehabilitation.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".