Spinal Tissue Loading Created by Different Methods of Spinal Manipulative Therapy Application
Bibliographic record
Abstract
STUDY DESIGN: Comparative study using robotic replication of spinal manipulative therapy (SMT) vertebral kinematics together with serial dissection. OBJECTIVE: The aim of this study was to quantify loads created in cadaveric spinal tissues arising from three different forms of SMT application. SUMMARY OF BACKGROUND DATA: There exist many distinct methods by which to apply SMT. It is not known presently whether different forms of SMT application have different effects on spinal tissues. Should the method of SMT application modulate spinal tissue loading, quantifying this relation may help explain the varied outcomes of SMT in terms of effect and safety. METHODS: SMT was applied to the third lumbar vertebra in 12 porcine cadavers using three SMT techniques: a clinical device that applies forces through a hand-held instrument (INST), a manual technique of applying SMT clinically (MAN) and a research device that applies parameters of manual SMT through a servo-controlled linear actuator motor (SERVO). The resulting kinematics from each SMT application were tracked optically via indwelling bone pins. The L3/L4 segment was then removed, mounted in a parallel robot and the resulting kinematics from SMT replayed for each SMT application technique. Serial dissection of spinal structures was conducted to quantify loading characteristics of discrete spinal tissues. RESULTS: In terms of load magnitude, SMT application with MAN and SERVO created greater forces than INST in all conditions (P < 0.05). Additionally, MAN and SERVO created comparable posterior forces in the intact specimen, but MAN created greater posterior forces on IVD structures compared to SERVO (P < 0.05). CONCLUSION: Specific methods of SMT application create unique vertebral loading characteristics, which may help explain the varied outcomes of SMT in terms of effect and safety. LEVEL OF EVIDENCE: N/A.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".