Morphology of longissimus thoracis and its clinical applications
Bibliographic record
Abstract
Current interest in understanding of spinal mechanics, etiology of back pain, and increased use of complex musculoskeletal models require detailed knowledge of the morphology of Longissimus Thoracis (LT) muscle. Methods This study utilized 28 embalmed cadavers using traditional anatomical dissections. Results Direct connections between LT and thoracic facet fibrous capsules were not found. Superior tendinous attachment of LT was at T2 vertebra in all specimens. At T2, T5 and T8 the LT medial tendons attached at the lateral third of the inferior margin of the transverse processes, whereas lateral tendons were found between the angle and tubercle of the ribs. Myofascial continuity of LT to posterior cervical musculature was found in all specimens. Lateral branches of the dorsal primary rami of the thoracic spinal nerves coursed through the muscle substance of LT in 31.5% of the identified nerves. Conclusion Our findings suggest that the LT does not have direct attachments to the fibrous capsules of thoracic facets, a point that should be considered in experimental models of thoracic facet joint structure. Continuity between the LT muscle and posterior cervical musculature suggests a mechanism of myofascial pain or mechanical dysfunction of these muscles. Perforation of LT by the lateral branches of the thoracic spinal nerves may provide a mechanism for neurogenic back pain associated with LT hypertonicity.
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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) | 0.003 | 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".