CAFFEINE INCREASES CENTRAL EXCITABILITY DURING A SUBMAXIMAL FATIGUE PROTOCOL
Bibliographic record
Abstract
Numerous observations in the literature suggest that caffeine and TMS could be used together to gain insight into the contributions of the central nervous system to neuromuscular fatigue. PURPOSE The current study examined the effects of caffeine on central excitability during a fatigue protocol using the quadriceps femoris muscle group. METHODS Subjects attended 2 laboratory sessions. Each session began with baseline data collection, including: maximal M wave and twitches, maximal voluntary contraction (MVC) verified with twitch interpolation, maximal surface EMG, and MEPs evoked during a 2.5% MVC. Subjects were then given a capsule containing caffeine (6 mg/kg) or placebo (randomized, double-blind, repeated measures). After a 1-h rest, baseline measures were repeated and a fatigue protocol commenced. The fatigue protocol consisted of sets of 10 isometric knee extension contractions. The 1st and 10th contraction of each set were maximal whereas contractions 2–9 were 50% MVC. Supramaximal shocks were applied to the femoral nerve on and immediately each maximal contraction. TMS was applied during contractions 2, 4, 6, and 8 at an output 10% above active motor threshold. Between each set 4 TMS stimuli were applied during a 2.5% MVC. This protocol continued until MVC fell by 40%. M wave, twitch, MVC and MEPs were recorded at 0, 2, 5, 10 and 15 min of recovery. RESULTS Caffeine increased the amplitude of MEPs elicited during the 50% contractions throughout the fatigue protocol (p < 0.01). However, the amplitude of MEPs evoked during the 2.5% MVC between sets was not affected by caffeine. The root mean square of the surface EMG during the 50% contractions was not affected by caffeine. CONCLUSION These data suggest that the caffeine enhances central excitability during voluntary effort, but that this effect is dependent on the extent of voluntary activation. Supported by NSERC (E.C.) and a Reebok Research Grant on Human Performance and Injury Prevention from the American College of Sports Medicine Foundation (J.K.)
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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) | 0.001 | 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) | 0.001 | 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".