Is a parallel elastic element responsible for the enhancement of steady-state muscle force following active stretch?
Bibliographic record
Abstract
For over 50 years, it has been recognised that muscles from many different species of animals are able to generate a higher steady-state isometric force after active stretch than during a purely isometric contraction at the same length. This is known as ;residual force enhancement' (rFE). The mechanism underlying this phenomenon remains controversial. One proposal is that an elastic element parallel to the cross-bridges becomes stiffer, or is engaged, when the muscle is activated and generates force when stretched. If this is indeed the sole mechanism, then rFE should be eliminated by subsequently shortening the muscle by a distance equal to or greater than the initial stretch. We tested this hypothesis using six intact single fibres from frog lumbrical muscle. The fibres were activated and stretched to generate rFE and then rapidly shortened by between 25% and 700% of the initial stretch distance. In contrast to previous reports, we found that rapid shortening induced a depression of subsequent isometric force. We used two methods to account for this force depression when calculating rFE, thereby obtaining upper and lower bounds for the true rFE. With both methods of calculation, rFE was significantly greater than zero when shortening distance was equal to stretch distance (P=0.0004 and P=0.03, respectively). Therefore, our hypothesis was not supported. We conclude that rFE is unlikely to be generated solely by a parallel elastic element.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".