Head Pre-Cooling Improves Symptoms of Heat-Sensitive Multiple Sclerosis Patients
Bibliographic record
Abstract
BACKGROUND: Damage to the central nervous system by Multiple Sclerosis (MS) leads to multiple symptoms, including weakness, ambulatory dysfunction, visual disturbances and fatigue. Heat can exacerbate the symptoms of MS whereas cooling can provide symptomatic relief. Since the head and neck areas are particularly sensitive to cold and cooling interventions, we investigated the effects of cooling the head and neck for 60 minutes on the symptoms of MS. METHODS: We used a double blinded, placebo controlled, cross-over study design to evaluate the effects of head and neck cooling on six heat-sensitive, stable, ambulatory females with MS (Extended Disability Status Scale 2.5-6.5). To isolate the effects of perceived versus physiological cooling, a sham cooling condition was incorporated, where subjects perceived the sensation of being cooled without any actual physiological cooling. Participants visited the clinic three times for 60 minutes of true, sham, or no cooling using a custom head and neck cooling hood, followed by evaluation of ambulation, visual acuity, and muscle strength. Rectal and skin temperature, heart rate, and thermal sensation were measured throughout cooling and testing. RESULTS: Both the true and sham cooling elicited significant sensations of thermal cooling, but only the true cooling condition decreased core temperature by 0.37 °C (36.97 ± 0.21 to 36.60 ± 0.23 °C). True cooling improved performance in the six minute walk test and the timed up-and-go test but not visual acuity or hand grip strength. CONCLUSIONS: Head and neck cooling may be an effective tool in increasing ambulatory capacity in individuals with MS and heat sensitivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 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".