Evaluation of cervical range of motion and isometric neck muscle strength: reliability and validity
Bibliographic record
Abstract
OBJECTIVE: To examine the test-retest reliability and construct validity of cervical active range of motion and isometric neck muscle strength as measured by the Multi Cervical Rehabilitation Unit (Hanoun Medical Inc., Ontario). DESIGN: A cross-sectional study. SETTING: Institutional practice. SUBJECTS: Twenty-one patients with neck pain and 25 healthy volunteers. METHODS: After a trial-run session, active range of motion (AROM) was measured in the subsequent two sessions, with 2-3 days in between. During each session, three measurements were taken for each direction (flexion, extension, lateral flexions and rotations). The measurement of isometric strength was after a 15-minute break following completion of the measurement of AROM. Three measurements were made for each of the six directions (flexion, extension, lateral flexions, protraction and retraction). The software of the Multi Cervical Rehabilitation Unit automatically recorded and calculated the maximum AROM and isometric strength. RESULTS: There was a good to high level of reliability in the measurement of AROM for both groups of subjects, with intraclass correlation coefficients (ICCs) ranging from 0.81 to 0.96. Results also demonstrated very good to excellent reliability in isometric strength measurement (ICCs ranged from 0.92 to 0.99). Moreover, there was a significant difference in isometric neck muscle strength (p = 0.001) and in AROM (p = 0.034) between the two groups. CONCLUSIONS: The Multi Cervical Rehabilitation Unit was found to be reliable and valid for testing the cervical active range of motion and isometric neck muscle strength for both normal and patient subjects.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".