Carbohydrate Mouth Rinse Counters Fatigue Related Strength Reduction
Bibliographic record
Abstract
The purpose was to determine the effect of carbohydrate (CHO) mouth rinse on maximal voluntary contraction (MVC) and neuromuscular output in a fatigued state. It was hypothesized that CHO mouth rinse would potentiate torque output in a fatigued state. In a double-blind, cross-over design, 12 competitive male athletes (9 rowers, 1 cyclist, 1 runner and 1 volleyball player) initially performed 3 × 5 s MVC isometric knee extensions followed by a 50% MVC contraction until volitional exhaustion, with quadriceps muscle activity measured via electromyography (EMG). Immediately after, either an 8% CHO maltodextrin (WASH), or noncaloric artificial sweetener (PLA) was mouth rinsed for 10sec, before 3 × 5 s final MVCs. Fatigue caused a significant decline in post fatigue MVC trial 1 for 3 s average torque (p = .03) and peak torque (p = .02) for PLA. This fatigue related decline in torque was not noticed for WASH, with a 2.5% and 3.5% less attenuation in peak and average torque, respectively in post fatigue MVC1 compared with PLA. The effect size for MVC trial 1 between WASH/PLA was seen to be small positive (ES = 0.22; 55% likelihood of positive). Overall for EMG RMS, there were no significant differences between PLA and WASH among all muscles. EMG median frequency showed comparable results between conditions with significant reductions due to fatigue. Taken together, this evidence suggests that the attenuation of torque post fatigue was less for CHO mouth rinse than a placebo. Even though the gains were marginal, these discoveries may play an important role in sport performance, as small performance effects can have significant outcomes in real-world competitions.
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 | 0.001 | 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".