Invisible and Visible Symptoms of Multiple Sclerosis
Bibliographic record
Abstract
The purpose of this study was to examine whether it is the invisible or the visible symptoms or signs of multiple sclerosis (MS) that are associated with greater health distress. Visible symptoms include the use of assistive devices, problems with balance, and speech difficulties, while invisible symptoms include fatigue, pain, depression, and anxiety. In a sample of 145 adults with MS, participants reported on these symptoms and their current level of self-reported health distress. Hierarchical regression analyses were used to determine whether invisible or visible symptoms were more predictive of health distress. When visible symptoms were added as the first step in the regression, 18% of the variance in health distress was explained. When invisible symptoms were added as the first step, 53% of the variance was accounted for. The invisible symptoms of pain and depression were the most significant predictors of distress. For a subset of the sample that had had MS for more than 11 years, pain and depression continued to be important predictors, but assistive-device use and fatigue were also important. Nurses should be aware that invisible symptoms may be more troubling to patients than visible symptoms and should ensure that adequate screening and treatment are provided for those with MS.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".