Accelerated long-term forgetting reveals everyday memory deficits in early-stage multiple sclerosis
Bibliographic record
Abstract
Abstract Background Patients with multiple sclerosis (MS) patients report subjective memory impairment (SMI) escaping routine neuropsychological testing. Accelerated long-term forgetting (ALF) refers to above average loss of information over an extended period of time (e.g., 7 days). This study investigates ALF in mildly affected MS patients and relates long-term memory performance to SMI. Methods This prospective study included 30 patients with early MS (mean EDSS ± SD = 1.1 ± 0.9) and 30 healthy controls (HC) matched for age and education. Participants underwent ALF testing [word list (RAVLT), geometric figure (RCF), logical memory (WMS)] at three time points (baseline, 30 min, 7 days). Cognition (Montreal Cognitive Assessment), depression, SMI and fatigue were assessed. The primary outcome (PO) was defined as the quotient of the 7-day score and the 30-min memory score for the verbal (RAVLT, WMS) and figural (RCF) memory tests. The study was approved by the local ethics committee and is registered in the German Register of Clinical Studies (DRKS00025791). Results MS patients showed impairments in PORAVLT (MS 0.66 ± 0.13 vs HC 0.82 ± 0.16; p < 0.001), whereas POWMS (MS 0.88 ± 0.15 vs HC 1.01 ± 0.12; p = 0.02) showed only a tendency. Regression analysis revealed significant associations for PORAVLT and fatigue (p = 0.034), and PORAVLT and SMI (p = 0.01) in patients but not in HC. Conclusion The ALF test quantifies SMI in MS-patients. With fatigue as a relevant associated factor, this fills the gap in objectifying SMI in MS for diagnostic purposes.
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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 | 0.001 | 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".