Assessment of Neck Pain and Its Associated Disorders
Bibliographic record
Abstract
STUDY DESIGN: Best evidence synthesis. OBJECTIVE: To critically appraise and synthesize the literature on assessment of neck pain. SUMMARY OF BACKGROUND DATA: The published literature on assessment of neck pain is large and of variable quality. There have been no prior systematic reviews of this literature. METHODS: The Bone and Joint Decade 2000-2010 Task Force on Neck Pain and Its Associated Disorders conducted a critical review of the literature (published 1980-2006) on assessment tools and screening protocols for traumatic and nontraumatic neck pain. RESULTS: We found 359 articles on assessment of neck pain. After critical review, 95 (35%) were judged scientifically admissible. Screening protocols have high predictive values to detect cervical spine fracture in alert, low-risk patients seeking emergency care after blunt neck trauma. Computerized tomography (CT) scans had better validity (in adults and elderly) than radiographs in assessing high-risk and/or multi-injured blunt trauma neck patients. In the absence of serious pathology, clinical physical examinations are more predictive at excluding than confirming structural lesions causing neurologic compression. One exception is the manual provocation test for cervical radiculopathy, which has high positive predictive value. There was no evidence that specific MRI findings are associated with neck pain, cervicogenic headache, or whiplash exposure. No evidence supports using cervical provocative discography, anesthetic facet, or medial branch blocks in evaluating neck pain. Reliable and valid self-report questionnaires are useful in assessing pain, function, disability, and psychosocial status in individuals with neck pain. CONCLUSION: The scientific evidence supports screening protocols in emergency care for low-risk patients; and CT-scans for high-risk patients with blunt trauma to the neck. In nonemergency neck pain without radiculopathy, the validity of most commonly used objective tests is lacking. There is support for subjective self-report assessment in monitoring patients' course, response to treatment, and in clinical research.
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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 | 0.045 | 0.171 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.010 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".