In vivo myograph measurement of muscle contraction at optimal length.
Bibliographic record
Abstract
BACKGROUND: Current devices for measuring muscle contraction in vivo have limited accuracy in establishing and re-establishing the optimum muscle length. They are variable in the reproducibility to determine the muscle contraction at this length, and often do not maintain precise conditions during the examination. Consequently, for clinical testing only semi-quantitative methods have been used. METHODS: We present a newly developed myograph, an accurate measuring device for muscle contraction, consisting of three elements. Firstly, an element for adjusting the axle of the device and the physiological axis of muscle contraction; secondly, an element to accurately position and reposition the extremity of the muscle; and thirdly, an element for the progressive pre-stretching and isometric locking of the target muscle. Thus it is possible to examine individual in vivo muscles in every pre-stretched, specified position, to maintain constant muscle-length conditions, and to accurately re-establish the conditions of the measurement process at later sessions. RESULTS: In a sequence of experiments the force of contraction of the muscle at differing stretching lengths were recorded and the forces determined. The optimum muscle length for maximal force of contraction was established. In a following sequence of experiments with smaller graduations around this optimal stretching length an increasingly accurate optimum muscle length for maximal force of contraction was determined. This optimum length was also accurately re-established at later sessions. CONCLUSION: We have introduced a new technical solution for valid, reproducible in vivo force measurements on every possible point of the stretching curve. Thus it should be possible to study the muscle contraction in vivo to the same level of accuracy as is achieved in tests with in vitro organ preparations.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".