Muscular Response to Physiologic Tensile Stretch of the Caprine C5/6 Facet Joint Capsule: Dynamic Recruitment Thresholds and Latencies
Bibliographic record
Abstract
This study examined the cervical muscle response to physiologic, high-rate (100 mm/s) tensile facet joint capsule (FJC) stretch. Six in-vivo caprine C5/6 FJC preparations were subjected to an incremental tensile loading paradigm. EMG activity was recorded from the right trapezius (TR) and multifidus (MF) muscle groups at the C5 and C6 levels; and from the sternomastoid (SM) and longus colli (LC) muscle groups bilaterally at the C5/6 level; during FJC stretch. Capsule load during the displacement applications was recorded via a miniature load cell, and 3D capsule strains (based on stereoimaging of an array of markers on the capsule surface) were reconstructed using finite element methods. EMG traces from each muscle were examined for onset of muscular activity. Capsule strains and loads at the time of EMG onset were recorded for each muscle, as was the time from the onset of FJC stretch to the onset of muscle activity. All muscles were responsive to physiologic high-rate FJC stretch. The deep muscles (MF and LC) were recruited at significantly smaller capsule loads and onset latencies than the superficial muscles (TR and SM). MF activation strain was significantly smaller than LC and TR activation strains. These data were also compared to previously published low-rate data. MF was the first muscle group to be recruited regardless of the activation criterion under consideration (i.e. strain, load, or latency) or the rate of FJC stretch. LC recruitment occurred significantly sooner under high-rate vs. low-rate FJC stretch. The results of this study provide further evidence of extensive ligamento-muscular reflex pathways between the FJC and the cervical musculature, which are responsive to both low-rate and high-rate FJC stretch. These data add to our knowledge of the dynamic response of paraspinal muscles relative to facet joint motion and provide a unique contribution to enhance the precision of computer-simulated impacts.
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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".