MétaCan
Menu
Back to cohort
Record W2001074110 · doi:10.1139/h10-032

Hypohydration and muscular fatigue of the thumb alter median nerve somatosensory evoked potentials

2010· article· en· W2001074110 on OpenAlexvenueno aff
Scott J. Montain, William J. Tharion

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2010
Typearticle
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsnot available
FundersU.S. Army
KeywordsIsometric exerciseSomatosensory evoked potentialMuscle fatigueSomatosensory systemMedicineMedian nerveAfferentAnesthesiaCardiologyPhysical medicine and rehabilitationElectromyographyInternal medicineSurgery

Abstract

fetched live from OpenAlex

The mechanisms by which dehydration impairs endurance performance remain unresolved but may involve alterations in afferent neural processing. The purpose of this study was to determine the effect of hypohydration on somatosensory evoked potentials (SEPs) at rest and during recovery from fatiguing exercise. Fourteen volunteers (12 men, 2 women) performed repetitive isometric thumb contractions (50% maximal voluntary contractions (MVC) and 100% MVC in a 5:1 ratio, each contraction separated by 5 s of rest) until exhaustion when euhydrated (EU) and when hypohydrated by 4% body mass (HY). SEPs were obtained from the median nerve. The results indicated that HY did not produce statistical differences in time to exhaustion (EU=754 (SD 255); HY=714 (SD 318) s; p=0.66) or rate of muscle fatigue. However, HY was associated with greater subjective feelings of fatigue and loss of vigor after exhaustive exercise (p<0.01). HY affected N20 latency with an interaction effect of hydration by fatigue state (EU-Rest: 18.5 (SD 1.6) ms; EU-Fatigue: 19.0 (SD 1.6) ms; HY-Rest: 18.3 (SD 1.3) ms; HY-Fatigue: 18.4 (SD 1.5) ms; p=0.034), but N20 and N20-P22 amplitude responses were similar between HY and EU trials. We concluded that moderate water deficits appear to alter afferent signal processing within the cerebral cortex.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.802
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.016
GPT teacher head0.261
Teacher spread0.244 · 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 designBench or experimental
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

Citations15
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueApplied Physiology Nutrition and MetabolismSame topicThermoregulation and physiological responsesFrench-language works237,207