In multiple sclerosis anxiety, not depression, is related to gender
Bibliographic record
Abstract
BACKGROUND: There is a high prevalence of depressive and anxiety disorders in multiple sclerosis (MS), a disease 2.5 times more frequent in females. Contrary to the general population, in whom studies have demonstrated higher rates of depression and anxiety in females, little is known about the impact of gender on psychiatric sequelae in MS patients. OBJECTIVES: We conducted a retrospective study to try to clarify this uncertainty. METHODS: Demographic, illness-related and behavioral variables were obtained from a neuropsychiatric database of 896 patients with a confirmed diagnosis of MS. Symptoms of depression and anxiety were obtained with the Hospital Anxiety and Depression Scale (HADS). Gender comparisons were undertaken and predictors of depression and anxiety sought with a linear regression analysis. RESULTS: HADS data were available for 711 of 896 (79.35%) patients. Notable gender differences included a higher frequency of primary progressive MS in males (p = 0.002), higher HADS anxiety scores in females (p < 0.001), but no differences in HADS depression scores. CONCLUSION: In MS, gender influences the frequency of anxiety only. This suggests that the etiological factors underpinning anxiety and depression in MS are not only different from one another, but also in the case of depression, different from those observed in general population samples.
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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 | 0.003 |
| 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) | 0.003 | 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".