Cervical Outcome Measures: Testing for Postural Stability and Balance
Bibliographic record
Abstract
BACKGROUND: Clinical tests assessing a correlation between structural pathology and cervical pain have been unsuccessful, leading the way for the development of functionally based tests. The purpose of this narrative is to review 4 promising functional tests for the assessment of sensorimotor dysfunction in patients with neck pain. The Joint Position Error/Head Repositioning Accuracy tests, and the Rod and Frame Test were reviewed. SPECIAL FEATURES: The SPNTT was developed to test proprioceptive mechanisms in the neck by applying torsion to mainly mechanoreceptors in the cervical spine. The Joint Position Error and Head Repositioning Accuracy test cervicocephalic kinesthesia or the ability to perceive both movement and position of the head in space related to the trunk. The Rod and Frame Test assesses patients' perception of the vertical orientation of their head in 3-dimensional space. All of these tests evaluate important mechanisms responsible for maintaining postural stability and balance and are thought to be applicable for use in mechanical neck pain patients. SUMMARY: All of the reviewed tests show clinical promise because they are able to distinguish patients with neck pain, particularly those with whiplash trauma and dizziness from asymptomatic controls. All of the tests assess cervical sensorimotor dysfunction, although considerably more research is needed to more clearly establish the psychometric properties for each test including minimal clinical important difference. Although these tests can be used in routine clinical practice, they should be used in combination with other related tests.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".