Tactile Perception of Pressure and Volitional Thrust Intensity Modulate Spinal Manipulation Dose Characteristics
Bibliographic record
Abstract
Objective The purpose of this study was to examine clinicians' ability to modulate spinal manipulation (SM) thrust characteristics based on their tactile perception of pressure and volitional intensity. Methods In a cross-sectional, within-participants design, 13 doctors of chiropractic delivered SM thrusts of perceived least, appropriate, or greatest intensity of their perceived safe output level for an SM thrust on low-fidelity thoracic spine models of 4 different pressure levels. The participants performed SM over the course of 96 trials in a randomized order on combinations of thrust intensity and pressure. Dependent variables included normalized preload force, thrust force, thrust duration, peak acceleration, time to peak acceleration, and displacement. For all dependent measures, 2-factor within-participants analysis of variance models with repeated measures on both factors were performed. Results Preload force increased with intensity ( F 2,24 = 9.72; P < .001) and model pressure ( F 3,36 = 4.27; P = .011). Participants modulated thrust force and displacement as each also increased with intensity escalation ( F 2,24 = 22.53, P < .001; F 2,18 = 45.20, P < .001). The highest accelerations were observed during the greatest intensity. Increased thrust force was delivered at higher model pressures ( F 3,36 = 6.43; P < .001). A significant interaction demonstrated that as volitional thrust intensity increased, greater displacement was attained, particularly on low pressure models ( F 6,54 = 11.06; P < .001). Thrust duration and time to peak acceleration yielded no significant differences. Conclusion Spinal manipulation thrust dosage was modulated by the chiropractors' tactile perception of pressure and volitional intensity.
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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 | 0.002 | 0.023 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".