Developing Biologically-Based Assessment Tools for Physical Therapy Management of Neck Pain
Bibliographic record
Abstract
SYNOPSIS: Neck pain is a common and episodic condition that is treated using a spectrum of interventions known to be moderately effective but is associated with a significant incidence of chronic pain. Recently, there has been increased focus on defining biological aspects of neck pain. Studies have indicated that neurophysiological, biomechanical, and motor control abnormalities are present and may be useful either in prognosis or classification. We review some of these findings in the context of our own work defining biological markers that may form the basis for clinical tests that can be used for prognosis, classification, or outcome evaluation in patients with neck pain. We have identified abnormalities in neurophysiology using quantitative sensory testing (vibration, touch, and current perception) and response to cold provocation that are related to neck disability. We have identified altered muscle biochemistry by measuring circulating muscle proteins in a lumbar surgery model and are now applying those methods to whiplash injury. We have incorporated capnography into treatment to address central physiological changes present in some patients by monitoring and training CO2 levels. We have developed an innovative new test, the Neck Walk Index, that captures abnormal control of head movement during slow gait as a means of differentiating patients with neck pain from either unaffected controls or individuals with other pathologies. We have used time-varying 3-dimensional joint orientation kinematics to assess deficits in motor control during an upper extremity reach task, the results showing that poor coordination and control of the shoulder girdle leads to shoulder guarding and inconsistencies in elbow joint movement. Despite some promising early results, future research is needed to determine how these measures help clinicians to diagnose, evaluate, and forecast future outcome for patients who present with neck pain. LEVEL OF EVIDENCE: Diagnosis, level 5.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".