Importance of sarcomere length when determining muscle physiological cross-sectional area: A spine example
Bibliographic record
Abstract
Muscle physiological cross-sectional area predicts the maximum capability of a muscle to generate isometric force. Biomechanical models often use estimates of individual muscle physiological cross-sectional area to partition internal forces among different muscles and predict joint forces and stability. In the spine literature, these physiological cross-sectional area values are generally obtained from imaging or cadaveric studies that have not accounted for a potential lengthened or shortened (and thus thinned or thickened, respectively) state of the muscles in question. Sarcomere length measurements can be used to normalize muscle lengths and correct for these length discrepancies. This article was designed to demonstrate potential effects of not accounting for instantaneous sarcomere length when calculating the physiological cross-sectional area of muscles of the spine region. Because some muscles of the spine region appear to be shortened and others lengthened in the neutral spine posture, both over- and under-estimations of physiological cross-sectional area are possible. Specifically, it is shown that the muscle physiological cross-sectional area could be over-estimated or under-estimated by as much as + 36% (multifidus) and -21% (rectus abdominis), respectively. This differential error effect poses difficulties in accurately estimating individual muscle forces and subsequent spine forces and stability that result from biomechanical models incorporating physiological cross-sectional area data obtained in the absence of sarcomere length measurements. Future work is needed to measure the dynamic range of sarcomere lengths of all spinal muscles to ensure correct inputs to biomechanical models.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".