An overview of systematic reviews investigating the accuracy, reliability, and relationships of tests and measures for diagnosis of neck pain.
Bibliographic record
Abstract
Background: Neck pain is a common condition and often difficult to differentiate and diagnose. Previous literature has investigated diagnostic accuracy of examination measures but is limited. Objective: To investigate examination measures for diagnosis of neck pain. Design: Umbrella review Literature Search: Four databases were searched electronically. Quality and risk of bias were assessed using the AMSTAR 2 and ROBIS. Diagnostic criteria were investigated for accuracy, reliability, and relationship to neck pain diagnoses. Study Selection Criteria: Systematic reviews of randomized clinical trials evaluating diagnostic criteria for neck pain. Data Synthesis: Twenty seven systematic reviews were included. Quantitative and qualitative results were summarized in narrative format. Results: Hand radiculopathy and numbness have good specificities (0.89-0.92) for facet and uncinate joint hypertrophy. 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 a positive ERT alone (+LR = 2.01; Sp = 0.59). Canadian C-spine Rules and Nexus low-risk 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: There are limited indicators providing strong diagnostic value for cervical spine diagnosis. Strength of recommendations are limited by heterogeneous outcomes, methodology, and classification systems. Future research should attempt to provide stronger recommendations of differential diagnostic criteria for pathoanatomical neck pain diagnoses.
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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 | 0.027 | 0.136 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.024 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".