Development of a Linked Segment Model to Derive Patient Low Back Reaction Forces and Moments During High-Velocity Low-Amplitude Spinal Manipulation
Bibliographic record
Abstract
OBJECTIVE: The purpose of this paper is to present the experimental setup, the development, and implementation of a new scalable model capable of efficiently handling data required to determine low back kinetics during high-velocity low-amplitude spinal manipulation (HVLA-SM). METHODS: The model was implemented in Visual3D software. All contact forces and moments between the patient and the external environment (2 clinician hand contact forces, 1 contact force between the patient and the treatment table), the patient upper body kinematics, and inertial properties were used as input. Spine kinetics and kinematics were determined from a single HVLA-SM applied to one healthy participant in a right side-lying posture to demonstrate the model's utility. The net applied force was used to separate the spine kinetic and kinematic time-series data from the HVLA-SM into preload as well as early and late impulse phases. RESULTS: Time-series data obtained from the HVLA-SM procedure showed that the participant's spine underwent left axial rotation, combined with extension, and a reduction in left lateral bending during the procedure. All components of the reaction force, as well as the axial twist and flexion/extension reaction moments demonstrated a sinusoidal pattern during the early and late impulse phases. During the early impulse phase, the participant's spine experienced a leftward axial twisting moment of 37.0 Nm followed by a rightward moment of -45.8 Nm. The lateral bend reaction moment exhibited a bimodal pattern during the early and late impulse phases. CONCLUSION: This model was the first attempt to directly measure all contact forces acting on the participant/patient's upper body, and integrate them with spine kinematic data to determine patient low back reaction forces and moments during HVLA-SM in a side-lying posture. Advantages of this model include the brevity of data collection (<1 hour), and adaptability for different patient anthropometries and clinician-patient contacts.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".