Preliminary investigation of spinal level and postural effects on thoracic muscle morphology with upright open<scp>MRI</scp>
Bibliographic record
Abstract
OBJECTIVE: Spinal-muscle morphological differences between weight-bearing and supine postures have potential diagnostic, prognostic, and therapeutic applications. While the focus to date has been on cervical and lumbar regions, recent findings have associated spinal deformity with smaller paraspinal musculature in the thoracic region. We aim to quantitatively investigate the morphology of trapezius (TZ), erector spinae (ES) and transversospinalis (TS) muscles in upright postures with open upright MRI and also determine the effect of level and posture on the morphological measures. METHODS: Six healthy volunteers (age 26 ± 6 years) were imaged (0.5 T MROpen, Paramed, Genoa, Italy) in four postures (supine, standing, standing with 30° flexion, and sitting). Two regions of the thorax, middle (T4-T5), and lower (T8-T9), were scanned separately for each posture. 2D muscle parameters such as cross-sectional area (CSA) and position (radius and angle) with respect to the vertebral body centroid were measured for the three muscles. Effect of spinal level and posture on muscle parameters was examined using 2-way repeated measures ANOVA separately for T4-T5 and T8-T9 regions. RESULTS: = .0009) at T9 than at T8. At T4-T5, the TZ CSA increased (up to 23%), and the ES and TS CSA decreased (up to 10%) in upright postures compared to supine. CONCLUSION: Geometrical parameters that describe muscle morphology in the thorax change with level and posture. The increase in TZ CSA in upright postures could result from greater activation while upright. The decrease in ES CSA in flexed positions likely represents passive stretching compared to neutral posture.
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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) | 0.003 | 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".