Neuromuscular determinants of explosive torque: Differences among strength‐trained and untrained young and older men
Bibliographic record
Abstract
This study compared the differences in neural and muscular mechanisms related to explosive torque in chronically strength-trained young and older men (>5 years). Fifty-four participants were allocated into four groups according to age and strength training level: older untrained (n = 14; 65.6 ± 2.9 years), older trained (n = 12; 63.6 ± 3.8 years), young untrained (n = 14; 26.2 ± 3.7 years), and young trained (n = 14; 26.7 ± 3.4 years). Knee extension isometric voluntary explosive torque (absolute and normalized as a percentage of maximal voluntary torque) was assessed at the beginning of the contraction (ie, 50, 100, and 150 ms-T50, T100, and T150, respectively), and surface electromyogram (sEMG) amplitude (normalized as a percentage of sEMG recorded during maximal voluntary isometric contraction) at 0-50, 50-100, and 100-150 time windows. Supramaximal electrically evoked T50 was assessed with octet trains delivered to the femoral nerve (8 pulses at 300 Hz). Voluntary T50, T100, and T150 were higher for trained than untrained in absolute (P < 0.001) and normalized (P < 0.030) terms, accompanied by higher sEMG at 0-50, 50-100, and 100-150 ms (P < 0.001), and voluntary T50/octet T50 ratio for trained. Greater octet T50 was observed for the young trained (P < 0.001) but not for the older trained (P = 0.273) compared to their untrained counterparts. Age effect was observed for voluntary T50, T100, and T150 (P < 0.050), but normalization removed these differences (P > 0.417). Chronically strength-trained young and older men presented a greater explosive torque than their untrained pairs. In young trained, the greater explosive performance was attributed to enhanced muscular and neural mechanisms, while in older trained to neural mechanisms only.
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) | 0.000 | 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) | 0.001 | 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".