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Thermoregulatory dysfunction in multiple sclerosis patients during moderate exercise in a thermoneutral environment (1104.17)

2014· article· en· W1717176843 on OpenAlexaff
Mu Huang, Nathan B. Morris, Ollie Jay, Scott L. Davis

Bibliographic record

VenueThe FASEB Journal · 2014
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMultiple sclerosisSudomotorMedicineCore temperatureThermoregulationInternal medicineCore (optical fiber)EndocrinologyCardiologyImmunology

Abstract

fetched live from OpenAlex

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

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.388
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.233
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2014
Admission routes1
Has abstractyes

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