Paraspinal muscle fibres demonstrate a complex relationship between contractile variability and function in canines with spinal degeneration
Bibliographic record
Abstract
Paraspinal muscle dysfunction is associated with spinal degeneration, yet our understanding of the specifics of this muscle dysfunction is incomplete. Muscles consist of thousands of individual fibres, but little is known about the variability in functional capability amongst these fibres, particularly in muscles that display pathological features. This study investigated individual muscle fibre contractile function variability within paraspinal muscle biopsies from 19 chondrodystrophic canines undergoing surgery for intervertebral disc extrusion, and a control group of 10 healthy rats. Specific force, active modulus, and rate of force redevelopment (KTR) were tested in an average of 20 fibres from each biopsy. Correlations were computed between means and both standard deviations (absolute variability) and coefficient of variations (relative variability) for each contractile variable. Across contractile variables, canine muscle fibres demonstrated greater relative variability and lower means compared to rat controls, confirming pathological impairment. Notably, most relationships demonstrated negative correlations between contractile performance and relative variability, consistent with the hypothesis that more impaired muscles exhibit greater fibre-level heterogeneity. Interestingly, two canines demonstrated unusually high KTR in type 1 fibres, suggesting possible compensatory adaptation. These findings highlight altered within-muscle contractile function in spinal pathology and underscore the importance of fibre-level analysis for understanding muscle dysfunction.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".