Obtaining force–frequency curves with a single 3‐second train of stimuli
Bibliographic record
Abstract
We devised a method to assess the force-frequency relationship (FFR) in human skeletal muscle that involved delivery of a single 2.8-s train of shocks directly to the femoral nerve. This increasing-frequency train (IFT) was based on a power function, with a range of stimulation frequencies beginning at 5 Hz and rising to 100 Hz. We compared the IFT to a standard series of constant-frequency trains (CFT) under two conditions. Force-frequency curves were examined, first in response to altered muscle length and second, following fatigue. There was no leftward shift in the curve when the knee extensors were shortened, although maximal force increased. In contrast, we observed a rightward shift in the curve after fatigue with both protocols; the frequency required to develop 50% of maximal force increased by 48% (P <.01) with CFT and 58% (P <.001) with an IFT. The CFT produced an irregular pattern of low-frequency fatigue recovery. In the IFT, low-frequency fatigue was greatest at the onset of recovery and decreased linearly until 120 s. These experiments show that the IFT protocol reveals alterations in muscle performance similar to the more traditional CFT. However, it requires only 2.8 s to administer and was judged more tolerable by 70% of our subjects. This suggests that the IFT may be an effective alternative for determining the FFR in human muscle for clinical and experimental purposes.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".