Long-term and Short-term Episodic Memory Dysfunction in Female MS Patients (PP-18)
Bibliographic record
Abstract
Multiple Sclerosis (MS), a myelin- targeting autoimmune disease of CNS, targets cognitive abilities in patients. The degree of this dysfunction would be different from one patient to another regarding their disease duration, age, EDSS, and lesion location in brain networks. The cognitive impairment will impact patients’ life adversely. Therefore, the present cross-sectional study aimed to evaluate the verbal episodic memory performance in Iranian Relapsing- Remitting Multiple Sclerosis (RRMS) patients and their healthy counterparts. Due to the susceptibility of women to MS, 35 female patients and 35 age, gender, and education-matched healthy controls were selected based on convenient sampling. The MS patients with Expanded Disability Status Scale (EDSS) scores ≤ 6 were recruited. The Montreal Cognitive Assessment (MoCA) and the California Verbal Learning Test (CVLT-II) were used for screening participants’ cognitive and verbal episodic memory function respectively. MoCA test (Persian version) was used to screen the participants for their cognitive function due to the higher sensitivity of the MoCA to the Mini-Mental State Exam (MMSE) for measuring cognitive function in individuals with MS. CVLT-II assess attention, learning strategies, recall accuracy and consistency, proactive and retroactive interference, recall errors, and recognition. The results revealed that MS patients were significantly impaired in the short and long-term recall as well as recognition list compared to their healthy counterparts. The findings showed that Multiple sclerosis has an adverse impact on verbal episodic memory 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".