The impact of sexual dysfunction on health-related quality of life in people with multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Sexual dysfunction is a prevalent symptom in multiple sclerosis (MS) that may affect patients' health-related quality of life (HrQoL). OBJECTIVE: The objective of this paper is to examine the impact of sexual dysfunction on HrQoL in a large national sample using The Multiple Sclerosis Intimacy and Sexuality Questionnaire-19 (MSISQ-19). METHODS: Participants were recruited from a large MS registry, the North American Research Committee on Multiple Sclerosis (NARCOMS) Registry. Participants self-reported demographic information and completed the Patient Determined Disease Steps (PDDS), MSISQ-19, and the Short Form-12 (SF-12). RESULTS: The study population included 6183 persons (mean age: 50.6, SD = 9.6; 74.7% female, 42.3% currently employed). Using multivariate hierarchical regression analyses, all variables excluding gender predicted both the physical component summary (PCS-12) and the mental component summary (MCS-12) of the SF-12. Scores on the MSISQ-19 uniquely accounted for 3% of the variance in PCS-12 scores while disability level, as measured by PDDS, accounted for 31% of the variance. Conversely, MSISQ-19 scores uniquely accounted for 13% of the variance in MCS-12 scores, whereas disability level accounted for less than 1% of the variance. CONCLUSION: In patients with MS, sexual dysfunction has a much larger detrimental impact on the mental health aspects of HrQoL than severity of physical disability.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".