Spinal Manipulation Impacts Cervical Spine Movement and Fitts' Task Performance: A Single-Blind Randomized Before-After Trial
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to determine if active cervical range of motion (ROM) and Fitts' task movement time differences occurred after high-velocity low-amplitude cervical spinal manipulation (SM) across various indexes of difficulty. METHODS: A single-blind randomized before-after trial was performed in a motor performance laboratory. Fifteen volunteers (21-42 years) with asymptomatic palpable intervertebral motion restriction at the C1-C2 level were randomly assigned to an SM group or to a no-intervention (NI) group. A single episode of upper cervical manipulation was performed on the SM group. Active cervical ROM and movement time were measured pre and posttreatment in the SM group and compared to similar measurements in the NI group. RESULTS: In the SM group, active cervical ROM into rotation increased after the intervention (pre, 74.75 degrees +/- 7.63 degrees ; post, 78.50 degrees +/- 7.23 degrees ; t (7) = -3.07; P < .02). During the second trial, significant group differences were present in the SM group for movement time in direction congruent conditions (F((8,48)) = 2.83; P < .02; eta(p)(2) = .320) and direction incongruent conditions (F((8,48)) = 2.31; P < .05; eta(p)(2) = .278) but not for the NI group. CONCLUSIONS: A linear relationship between indexes of difficulty and movement time as predicted by Fitts' law was observed. Significant group effects indicate that SM not only increases cervical active ROM but also facilitates the performance of a cervical spine Fitts' task requiring rotation. This task may be used to quantify motor performance in clinically symptomatic populations with reduced ROM who are appropriate candidates for SM.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".