Impact of the Cognitive Status on the Memory Complaints in MS Patients
Bibliographic record
Abstract
OBJECTIVE: Despite the evidence of cognitive deficits in Multiple Sclerosis (MS) patients, evaluation of their cognitive integrity is often limited to the use of clinical interviews and questionnaires. However, the consensus in the literature is that these patients under- or overestimate their deficits and repercussions. The objective of this study was to clarify why some patients overestimate while others underestimate their memory deficits. METHOD: Fifty-four participants (30 MS, 24 controls) completed the Prospective and Retrospective Memory Questionnaire (PRMQ) and were tested on a battery of neuropsychological tests. Based on the test results, MS patients were categorized as having either mild or moderate/severe cognitive deficits. RESULTS: The moderate/severe MS group differed from the two other groups on the Rey Auditory Verbal Learning Test (RAVLT) but did not differ from the control group on the PRMQ. Conversely, the mild MS group did not differ from the control group on the RAVLT but did report significantly more problems than this group on the PRMQ. There was no difference between the two clinical groups on the Depression Index (Beck) but there was a significant correlation (r=.409) between the depression scores and the overestimation of prospective memory problems (PRMQ). CONCLUSION: The results explain the contradiction in the literature. It is the mild group who overestimates, maybe because they are overly concerned by their deficits, whereas the cognitive impairments of the moderate/severe group lead them to underestimate and may make their self-assessment unreliable. Formal testing or information from a significant other would be advisable.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".