Neural Responses to the Mechanical Parameters of a High-Velocity, Low-Amplitude Spinal Manipulation: Effect of Preload Parameters
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to determine how the preload that precedes a high-velocity, low-amplitude spinal manipulation (HVLA-SM) affects muscle spindle input from lumbar paraspinal muscles both during and after the HVLA-SM. METHODS: Primary afferent activity from muscle spindles in lumbar paraspinal muscles were recorded from the L6 dorsal root in anesthetized cats. High-velocity, low-amplitude spinal manipulation of the L6 vertebra was preceded either by no preload or systematic changes in the preload magnitude, duration, and the presence or absence of a downward incisural point. Immediate effects of preload on muscle spindle responses to the HVLA-SM were determined by comparing mean instantaneous discharge frequencies (MIF) during the HVLA-SM's thrust phase with baseline. Longer lasting effects of preload on spindle responses to the HVLA-SM were determined by comparing MIF during slow ramp and hold movement of the L6 vertebra before and after the HVLA-SM. RESULTS: The smaller compared with the larger preload magnitude and the longer compared with the shorter preload duration significantly increased (P = .02 and P = .04, respectively) muscle spindle responses during the HVLA-SM thrust. The absence of preload had the greatest effect on the change in MIF. Interactions between preload magnitude, duration, and downward incisural point often produced statistically significant but arguably physiologically modest changes in the passive signaling properties of the muscle spindle after the manipulation. CONCLUSION: Because preload parameters in this animal model were shown to affect neural responses to an HVLA-SM, preload characteristics should be taken into consideration when judging this intervention's therapeutic benefit in both clinical efficacy studies and in clinical practice.
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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) | 0.000 | 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) | 0.002 | 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".