Understanding changes in peripheral and central excitability following submaximal \ncontractions
Bibliographic record
Abstract
The objective of this thesis was to examine the effects of brief (2s), non-fatiguing, \nsubmaximal (50% of MVC) and intermittent (2s on, 2s off) contractions on measures of central \nand peripheral excitability. Nine resistance-trained males completed a contraction protocol \nconsisting of 5 such contractions of the elbow flexors. Pre-, immediately post-, and 5 minutes \npost-contractions the participants received transcranial magnetic stimulation (TMS), \ntransmastoid electrical stimulation (TMES), peripheral nerve stimulation, and motor point \nstimulation to elicit motor-evoked potentials (MEPs), cervicomedullary-evoked potentials \n(CMEPs), maximal muscle compound action potentials (Mmax), and peak twitch force (PT), \nrespectively. All MEPs and CMEPs were normalized to Mmax. In addition, correlations between \ncentral and peripheral excitability were analyzed in order to determine if the two are separate \nentities or related. Finally, all measurements were taken both at rest, as well as during a slight \n(5% of MVC) contraction. This allowed us to determine if the changes in central and peripheral \nexcitability, as well as the correlations between the two, were state-dependent. Results showed \nan increase in corticospinal excitability (CSE) that was state-dependent, a decrease in spinal \nexcitability that was not state-dependent, and an increase in muscle excitability that was not \nstate-dependent following the contraction protocol. There was a positive correlation between \nCSE and peripheral excitability that was state-dependent, and a negative correlation between \nspinal excitability and peripheral excitability that was not state-dependent. Since some of the \ntrends observed were state-dependent, the major finding of this thesis is that results obtained at \nrest should not be generalized to movement situations.
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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) | 0.000 | 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) | 0.002 | 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".