Influence of Spinal Manipulative Therapy Force Magnitude and Application Site on Spinal Tissue Loading: A Biomechanical Robotic Serial Dissection Study in Porcine Motion Segments
Bibliographic record
Abstract
OBJECTIVE: In order to define the relation between spinal manipulative therapy (SMT) input parameters and the distribution of load within spinal tissues, the aim of this study was to determine the influence of force magnitude and application site when SMT is applied to cadaveric spines. METHODS: In 10 porcine cadavers, a servo-controlled linear actuator motor provided a standardized SMT simulation using 3 different force magnitudes (100N, 300N, and 500N) to 2 different cutaneous locations: L3/L4 facet joint (FJ), and L4 transverse processes (TVP). Vertebral kinematics were tracked optically using indwelling bone pins, the motion segment removed and mounted in a parallel robot equipped with a 6-axis load cell. The kinematics of each SMT application were replicated robotically. Serial dissection of spinal structures was conducted to quantify loading characteristics of discrete spinal tissues. Forces experienced by the L3/L4 segment and spinal structures during SMT replication were recorded and analyzed. RESULTS: Spinal manipulative therapy force magnitude and application site parameters influenced spinal tissues loading. A significant main effect (P < .05) of force magnitude was observed on the loads experienced by the intact specimen and supra- and interspinous ligaments. The main effect of application site was also significant (P < .05), influencing the loading of the intact specimen and facet joints, capsules, and ligamentum flavum (P < .05). CONCLUSION: Spinal manipulative therapy input parameters of force magnitude and application site significantly influence the distribution of forces within spinal tissues. By controlling these SMT parameters, clinical outcomes may potentially be manipulated.
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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 | 0.000 | 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) | 0.001 | 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".