The kinematics of motion palpation and its effect on the reliability for cervical spine rotation
Bibliographic record
Abstract
BACKGROUND: The reliability of a test depends on its standardization. Instrumental measurement of the reproducibility of the test is an effective way to evaluate the level of standardization obtained. Improved standardization is believed to yield greater reliability. OBJECTIVE: The objectives of this study were to measure the technical ability of an examiner to reproduce the kinematics of motion palpation for cervical spine rotation and to evaluate the effect of standardization on the reliability of the test. DESIGN: A study of reproducibility of the kinematics of the test for cervical spine rotation was conducted by means of a computerized system of analysis of movement. The reliability when reproducibility was achieved was compared with reliability when it failed. RESULTS: The data collected enable us to establish a standardized protocol for the execution of the test. The standardized palpation is executed within 6 of inclination from the pure plane of rotation. The successful reproduction of the kinematics of the test raises its reliability to detect the presence of fixations (kappa raising from 0.337 and 0.352 to 0.682). CONCLUSIONS: A greater reliability, arising from a high level of reproducibility, enables us to document the advantages of the standardization of motion palpation in chiropractic.
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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 | 0.039 | 0.199 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".