Thoracic Kyphosis Affects Spinal Loads and Trunk Muscle Force
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Patients with increased thoracic curvature often come to physical therapists for management of spinal pain and disorders. Although treatment approaches are aimed at normalizing or minimizing progression of kyphosis, the biomechanical rationales remain unsubstantiated. SUBJECTS: Forty-four subjects (mean age [+/-SD]=62.3+/-7.1 years) were dichotomized into high kyphosis and low kyphosis groups. METHODS: Lateral standing radiographs and photographs were captured and then digitized. These data were input into biomechanical models to estimate net segmental loading from T2-L5 as well as trunk muscle forces. RESULTS: The high kyphosis group demonstrated significantly greater normalized flexion moments and net compression and shear forces. Trunk muscle forces also were significantly greater in the high kyphosis group. A strong relationship existed between thoracic curvature and net segmental loads (r =.85-.93) and between thoracic curvature and muscle forces (r =.70-.82). DISCUSSION AND CONCLUSION: This study provides biomechanical evidence that increases in thoracic kyphosis are associated with significantly higher multisegmental spinal loads and trunk muscle forces in upright stance. These factors are likely to accelerate degenerative processes in spinal motion segments and contribute to the development of dysfunction and pain.
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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) | 0.005 | 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".