The Effects of Load and Training Pattern on Acute Neuromuscular Responses in the Upper Body
Bibliographic record
Abstract
The purpose of this study was to examine the effects of repetition maximum (RM) loads and training patterns on acute neuromuscular responses in the upper body. Markers of fatigue were monitored under a descending pattern (DP), in which repetitions decreased in subsequent sets, and an ascending pattern (AP), in which repetitions increased in subsequent sets. Both training patterns were performed using 5- and 10-RM loads. Fatigue was assessed by monitoring changes in force output, motor unit activation and muscle twitch characteristics (peak twitch [PT], time to PT [TPT], and ½ relaxation time [RT]). All 4 protocols (5-RM DP, 5-RM AP, 10-RM DP, and 10-RM AP) produced significant decreases pre to postprotocol in force output, TPT, and ½RT. With the exception of 5-RM DP, all protocols produced significant decreases in motor unit activation. Pre to postprotocol, PT forces were potentiated under 5-RM loads, whereas they were depressed under 10-RM loads. Hence, a main effect for training protocols showed that changes in PT force were significantly different under 5-RM, as compared to 10-RM loads. The results indicate that central fatigue may be independent of load and pattern, whereas peripheral fatigue would appear to be dependent on load but not pattern.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".