Why Sex Matters: A Cognitive Study of People With Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND: Cognitive dysfunction affects 40% to 70% of people with multiple sclerosis (MS). Sex may influence a person's cognition. Although a few studies have reported greater cognitive deficits in men than women, it is unclear whether specific cognitive domains are more vulnerable than others to the effects of sex or whether cognition is influenced by neurologic or psychiatric variables. METHODS: A chart review was undertaken of 408 people with MS referred to neuropsychological services. Demographic and MS-related variables were extracted from the patients' records. We used the Minimal Assessment of Cognitive Functioning in Multiple Sclerosis for the neuropsychological assessment. Raw test scores were converted to z scores using Canadian regression-based normative means. A general linear model was conducted on the adjusted scores, controlling for age; years of education; disease course; illness duration; and disability, anxiety, and depression scores. RESULTS: Men were more likely than women to have primary progressive MS (χ=6.415, P=0.011). There were no other sex differences with respect to demographic, neurologic, or psychiatric data. Women performed significantly better than men on the California Verbal Learning Test-Second Edition Total Learning index (F=7.846, P=0.006). CONCLUSIONS: An analysis of a large, consecutive sample of people with MS demonstrated that sex, independent of demographic, neurologic, or psychiatric factors, is an important determinant in cognitive impairment, with men being more impaired than women on tests of verbal learning and memory.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".