Kinematic measures to objectify head and neck motions in palpatory diagnosis: a pilot study.
Bibliographic record
Abstract
CONTEXT: Physicians typically combine the use of palpation and objective measures, as evidence-based medicine dictates, to improve patient diagnosis and care. Practitioners also use palpatory examination in manual medicine to diagnose musculoskeletal impairment; however, there are no commonly accepted objective measures to complement palpatory findings. OBJECTIVE: To evaluate coupled vertebral motion as a parameter to complement palpatory findings from a standard clinical diagnostic test of cervical function. METHODS: Two examiners performed a blind screening of volunteer subjects for the presence of palpable symmetry or asymmetry in motions of the head and neck. In cases of interexaminer agreement, subjects then participated in kinematic assessment of cervical motion patterns. Neck angles were recorded, plotted, and evaluated for amounts of vertebral coupling. RESULTS: Interexaminer agreement was reached with 18 of the 34 subjects screened. Seven subjects with symmetric responses constituted the control group. Experimental subjects consisted of an asymmetric-asymptomatic (pain-free) group (n=6) and an asymmetric-symptomatic (pain) group (n=5). Control subjects exhibited the smallest average linear slope (-0.32) for the least amount of coupled motion. The average linear slopes for asymmetric subjects was -0.42 (asymptomatic) and -0.50 (symptomatic). Data analysis revealed that statistically significant differences among groups will be detected with a larger sample size. CONCLUSIONS: Objective, kinematic parameters can be generated, measured, and evaluated relative to palpatory findings of musculoskeletal impairment by identifying trends in ratios of cervical lateral flexion and axial rotation.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".