The examination of soft tissue compliance in the thoracic region for the development of a spinal manipulation training mannequin.
Bibliographic record
Abstract
PURPOSE: To determine if the soft tissue compliance of the thoracic paraspinal musculature differs based on gender and body type to help create a foam human analogue mannequin to assist in the training of spinal manipulative therapy. METHODS: 54 volunteers were grouped based on their gender and body types. In the prone position, thoracic paraspinal soft tissue compliance was measured at T1, T3 T6, T9 and T12 vertebrae levels bilaterally using a tissue compliance meter. RESULTS: There was no significant difference in tissue compliance when comparing the genders except at T1 (p=0.026). When comparing body types, significantly higher tissue compliance was found between endomorphs and the other groups. No significant difference was found between ectomorphs and mesomorphs. The compliance for the participants in this study ranged from 0.122 mm/N to 0.420 mm/N. CONCLUSION: There are significant differences in thoracic spine soft tissue compliance in healthy asymptomatic patients between genders in the upper thoracic spine, and between different body types throughout the thoracic spine. It may be beneficial to create multiple versions of practice mannequins to simulate variations amongst different patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.002 | 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".