A Kinematic Model to Assess Spinal Motion During Walking
Bibliographic record
Abstract
In Brief Study Design and Objectives. A 3-dimensional multi-segment kinematic spine model was developed for noninvasive analysis of spinal motion during walking. Preliminary data from able-bodied ambulators were collected and analyzed using the model. Summary of Background Data. Neither the spine’s role during walking nor the effect of surgical spinal stabilization on gait is fully understood. Typically, gait analysis models disregard the spine entirely or regard it as a single rigid structure. Data on regional spinal movements, in conjunction with lower limb data, associated with walking are scarce. Methods. KinTrak software (Motion Analysis Corp., Santa Rosa, CA) was used to create a biomechanical model for analysis of 3-dimensional regional spinal movements. Measuring known angles from a mechanical model and comparing them to the calculated angles validated the kinematic model. Spine motion data were collected from 10 able-bodied adults walking at 5 self-selected speeds. These results were compared to data reported in the literature. Results. The uniaxial angles measured on the mechanical model were within 5° of the calculated kinematic model angles, and the coupled angles were within 2°. Regional spine kinematics from able-bodied subjects calculated with this model compared well to data reported by other authors. Conclusions. A multi-segment kinematic spine model has been developed and validated for analysis of spinal motion during walking. By understanding the spine’s role during ambulation and the cause-and-effect relationship between spine motion and lower limb motion, preoperative planning may be augmented to restore normal alignment and balance with minimal negative effects on walking. A 3-dimensional multi-segment kinematic spine model has been developed and validated for analysis of spinal motion during walking. Preliminary data from able-bodied ambulators were collected and analyzed. By understanding the manner in which the spine contributes to walking, preoperative planning may be augmented so as to restore normal alignment and balance with minimal negative effects on gait.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".