Occupational Attainment as a Marker of Cognitive Reserve in Multiple Sclerosis
Bibliographic record
Abstract
Cognitive dysfunction affects half of MS patients. Although brain atrophy generally yields the most robust MRI correlations with cognition, significant variance in cognition between individual MS patients remains unexplained. Recently, markers of cognitive reserve such as premorbid intelligence have emerged as important predictors of neuropsychological performance in MS. In the present study, we aimed to extend the cognitive reserve construct by examining the potential contribution of occupational attainment to cognitive decline in MS patients. Brain atrophy, estimated premorbid IQ, and occupational attainment were assessed in 72 MS patients. The Minimal Assessment of Cognitive Functioning in MS was used to evaluate indices of information processing speed, memory, and executive function. Results showed that occupational attainment was a significant predictor of information processing speed, memory, and executive function in hierarchical linear regressions after accounting for brain atrophy and premorbid IQ. These data suggest that MS patients with low occupational attainment fare worse cognitively than those with high occupational attainment after controlling for brain atrophy and premorbid IQ. Occupation, like premorbid IQ, therefore may make an independent contribution to cognitive outcome in MS. Information regarding an individual's occupation is easily acquired and may serve as a useful proxy for cognitive reserve in clinical settings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".