Further characterization and validation of the oxygen uptake efficiency slope for persons with multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: To further characterize the oxygen uptake efficiency slope (OUES) in persons with multiple sclerosis through a direct comparison with matched controls, and by examining differences across the multiple sclerosis disability spectrum. Also, to validate the OUES as an alternative method, which can be derived from submaximal exercise testing, for expressing cardiorespiratory fitness in persons with mild-to-severe multiple sclerosis. PARTICIPANTS: A total of 62 participants (Expanded Disability Status Scale (EDSS) = 1.5-6.5) with MS and 21 non-multiple sclerosis controls completed a symptom-limited cardiopulmonary exercise test. RESULTS: The OUES was significantly lower in persons with multiple sclerosis (mean 1,708.5 (standard deviation (SD) 503.7)) compared with non-multiple sclerosis controls (mean 2074.2 (SD 823.2)). With regards to the multiple sclerosis sample, there was a significant difference in the OUES (F[2,59] = 8.9, p < 0.001, ηρ2 = 0.23) across the multiple sclerosis disability spectrum. The OUES was significantly correlated with both OUES50 (r = 0.86) and OUES75 (r = 0.91), and Bland-Altman plots demonstrated agreement between OUES and submaximal OUES values. CONCLUSION: Overall, the OUES is a viable method for expressing cardiorespiratory fitness in individuals with multiple sclerosis, and submaximal OUES is an appropriate alternative when maximal exercise testing is not feasible.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".