Characteristics of Forces at the Clinician–Patient and Patient–Table Interfaces During Thoracic Spinal Manipulation in Asymptomatic Adults Are Consistent With Deformable Body Models
Bibliographic record
Abstract
Investigating all forces exerted on the patient's body during high-velocity, low-amplitude spinal manipulative therapy (SMT) remains fundamental to elucidate how these may contribute to SMT's effects. Previous conflicting findings preclude our understanding of the relationship between SMT forces acting at the clinician-patient and patient-table interfaces. This study aimed to quantify forces at the clinician-participant and participant-table interfaces during thoracic SMT in asymptnomatic adults. An experienced clinician provided a posterior to anterior SMT centered to T7 transverse processes using predetermined force-time characteristics to 40 asymptomatic volunteers (20 females; average age = 27.2 [4.9] y). Forces at the clinician-participant interface were recorded by triaxial load cells; whereas, forces at the participant-table interface were recorded by the force-sensing table technology. Preload force, total peak force, time to peak, and loading rate at each interface were analyzed descriptively. Total peak vertical forces at the clinician-participant interface averaged 532 (71) N while total peak forces at the participant-table interface averaged 658 (33) N. Forces at the participant-table interface were, on average, 1.27 (0.25) times larger than the ones at the clinician-participant interface. Larger forces at the participant-table interface compared with the ones at the clinician-participant interface during thoracic SMT are consistent with mathematical models developed to investigate thoracic impact simulating a dynamic force-deflection response.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".