An overview of systematic reviews investigating clinical features for diagnosing neck pain and its associated disorders
Bibliographic record
Abstract
BACKGROUND: Neck pain is a common condition that is often difficult to diagnose. Previous literature has investigated diagnostic accuracy of examination measures, but the strength and clinical applicability are limited. This overview of systematic reviews aimed to investigate clinical features for diagnosing neck pain and its associated disorders. METHODS: An overview of systematic reviews was conducted searching four electronic databases for systematic reviews evaluating diagnostic criteria for neck pain. Quality and risk of bias were assessed using the AMSTAR 2 and ROBIS. Clinical features for neck pain were investigated for diagnostic utility. RESULTS: Twenty-seven systematic reviews were included. Hand radiculopathy and numbness have good specificities (0.89-0.92) for facet and uncinate joint hypertrophy. For facet-related dysfunction, the extension rotation test (ERT) and manual assessment have good sensitivities and moderate-good specificities. Positive ERT combined with positive manual assessment findings (+LR = 4.71; Sp = 0.83) improves diagnostic accuracy compared to positive ERT alone (+LR = 2.01; Sp = 0.59). Canadian C-spine Rules and Nexus criteria have excellent validity in screening for cervical fracture or instability. Imaging appears to have validity in diagnosing ligamentous disruption or fractures but lacks clarity on predicting future neck pain. Increased fatty infiltrates have been found with whiplash-associated disorders and mechanical neck pain. CONCLUSIONS: This review found limited indicators providing strong diagnostic utility for diagnosing neck pain. Strength of recommendations are limited by heterogeneous outcomes, methodology, and classification systems. Future research should investigate new differential diagnostic criteria for specific structures contributing to neck pain.
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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 | 0.021 | 0.101 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.026 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".