Thermoregulatory dysfunction in multiple sclerosis patients during moderate exercise in a thermoneutral environment (1104.17)
Bibliographic record
Abstract
Impairments in sudomotor function during passive heat stress have been reported in multiple sclerosis (MS), a demyelinating disease of the CNS that disrupts autonomic function. However, little is known regarding exercise induced increases in core body temperature on thermoregulatory mechanisms in MS. Thus, the aim of this study was to test the hypothesis that thermoregulatory function is impaired in MS patients compared to healthy controls (CN) during moderate exercise. Thermoregulatory function in five patients diagnosed with relapsing‐remitting MS and five mass‐matched healthy controls were compared during a single bout of cycling exercise (fixed workload of 70 Watts) for 30‐60 minutes in a climate‐controlled room (25°C, 30% RH). Sweating thermosensitivity (MS: 0.56±0.15 vs CN: 0.81±0.13, p=0.04) was significantly lower while a delay in sweating onset time (MS: 14.8±10.0 min vs CN: 5.6±1.6 min, p=0.07) approached significance in MS patients compared to controls. These altered mechanisms of body temperature regulation likely contributed to a greater observed change in core body temperature measured rectally (MS: 0.84±0.34 °C vs CN: 0.37±0.27 °C, p=0.04) in patients with MS. This observed thermoregulatory dysfunction in MS patients may intensify disease symptoms limiting exercise tolerance. Grant Funding Source : Kuzell Institute and National MS Society Grant RG4043A1/1
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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 | 0.000 |
| 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) | 0.001 | 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".