Efficacy of different biomechanical strategies for modulating force–time parameters of high-velocity low-amplitude manipulation of the thoracic spine: a randomized crossover experimental study
Bibliographic record
Abstract
BACKGROUND: Manual therapy, including high-velocity low-amplitude spinal manipulation (HVLA-SM), is a complex motor task performed by trained individuals. The ability to modulate the magnitude of applied forces is an attribute of proficiency that is challenging for providers and students. Adopting different biomechanical strategies may facilitate force modulation by practitioners performing HVLA-SM. This study evaluated the efficacy of different biomechanical strategies on force-time characteristics of prone thoracic HVLA-SM. METHODS: A randomized crossover experimental design was used. Data were collected between October 2022 and May 2023 from chiropractic students at the Canadian Memorial Chiropractic College who performed HVLA-SM targeted to the thoracic spine of a prone-lying manikin using as much force as possible in each of six different strategies. Strategies (S1 to S6) were specifically developed to successively increase a person's ability to produce force while performing HVLA-SM. Force-time parameters for the HVLA-SM trials were recorded. Peak force was the primary outcome of interest while preload force, load rate, and time to peak force were analyzed as secondary measures. RESULTS: Data were collected from 97 participants (51 female). Peak force increased successively from S1 to S5 with moderate effects (- 0.45 ≤ effect size ≤ -0.72). There was no statistical difference in either peak force or load rate between S5 and S6. Load rate also did not statistically increase between S3 and S4 where different muscle groups were targeted to produce force. The strategy with the highest peak force (S6) also demonstrated the lowest preload force. CONCLUSIONS: Strategies used in this study effectively facilitated modulation of force-time characteristics of prone thoracic HVLA-SM. Thus, training approaches may consider introducing people to different biomechanical strategies to enhance HVLA-SM force modulation.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".