Voluntary Activation At Short And Long Muscle Lengths In The Human Elbow Extensors
Bibliographic record
Abstract
PURPOSE: To evaluate whether muscle length (shortened, or slack versus lengthened muscle) affected voluntary activation when calculated using the twitch interpolation method and compared to two extrapolation methods. METHODS: Twelve healthy men [mean age 26.9(4.3)yrs] performed elbow extensor voluntary contractions at short (20° of elbow flexion) and long muscle lengths (120°). In each condition, doublets were evoked during 5s voluntary contractions at 5%, 10%, 20%, 40%, 60%, 80%, 100% of maximum voluntary contraction torque (MVC), and at rest post-MVC. Voluntary activation at each length was calculated using the formula: 1-(interpolated doublet/post-MVC doublet) x100%. Because the post-MVC doublet amplitude is reduced in the shortened position, voluntary activation also was estimated using linear and non-linear extrapolations of voluntary and interpolated torques at values greater than 20% of MVC. RESULTS: MVC torques were similar at 20° (57 Nm), and 120° (52 Nm). Interpolated doublet amplitude (0.8 Nm) was unaffected by joint angle, but the post-MVC doublet torque (normalized to MVC torque) was 32% lower at 20° than at 120°. Non-linear extrapolations created a 24% increase in post-MVC doublet amplitudes versus a 6% increase estimated from linear extrapolations. Using the predicted post-MVC doublet at the short length improved voluntary activation by 18-33% for submaximal contraction intensities (< 60% of MVC). However, maximal voluntary activations (during MVCs) when calculated, or predicted by both linear and non-linear extrapolations were not affected by muscle length changes (94-96%). CONCLUSION: Voluntary activation of the elbow extensors was length-dependent with lower voluntary activation at submaximal intensities at short lengths. Compared to the calculated twitch interpolation method, in this muscle group, non-linear extrapolation improved the ability to assess voluntary activation below 60% of MVC by accounting for the diminished post-MVC doublet amplitude in a shortened length. Supported by NSERC.
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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) | 0.000 | 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".