Predictors and health impact of exercise capacity in multiple sclerosis
Bibliographic record
Abstract
Multiple Sclerosis (MS) is a chronic disease with an unpredictable course that impacts significantly on physical performance. Exercise or fitness has become an essential part of management and health promotion for persons with MS. The gold standard measure of exercise capacity (VO2peak) is the maximal exercise test, a graded test that involves an increase in workload until exhaustion is reached. Although this test is the most accurate measure of exercise capacity, it is not clinically useful. Therefore, the main objective of this cross-sectional study is to estimate the extent to which exercise capacity can be predicted by sub-maximal tests in persons with MS. By using data from several functional sub-maximal tests, a regression equation was formulated to estimate the exercise capacity of persons with MS. The results indicated that the modified Canadian Aerobic Fitness Test (mCAFT), grip strength and body weight explained 74% of the variability in VO2peak. Furthermore, MS literature has shown that health-related quality of life (HRQL) is greatly reduced in MS, as it impacts health perception and capacity to perform daily activities. Therefore, improving HRQL has become an important goal of all health care interventions. It is essential to evaluate and understand patients' own perceptions of the impact of symptoms on their overall health status and their well-being. Thus, the objective of the second manuscript was to estimate the extent to which physical capacity predicts perceived health status in persons with MS. Using multiple linear regression the following variables: sex, vitality, pain, smoking status, walking capacity, social functioning and cognition emerged as significant predictors of the outcome explaining approximately 50% of perceived health status. Significant interaction terms between sex and pain, as well as between sex and vitality were found, indicating that the contributions to perceive
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".