Clinical Diagnostic Tests versus Medial Branch Blocks for Adults with Persisting Cervical Zygapophyseal Joint Pain: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Purpose: A systematic review and meta-analysis were performed to identify clinical tests for diagnosing cervical zygapophyseal joint pain (CZP) and to determine their diagnostic accuracy. Method: A search strategy was carried out to find relevant evidence published in CINAHL, Embase, MEDLINE, and PEDro from 1980 to January 1, 2015, pertaining to the clinical diagnosis of CZP. Quality assessment was completed using the Quality Assessment of Diagnostic Accuracy Studies–2. Results were analyzed to pool sensitivity and specificity and clarify diagnostic value. Results: Seven articles (n=463) were included for data synthesis and review. Intersegmental mobility tests were found to have the highest diagnostic accuracy, with pooled sensitivity of 0.91 (95% CI: 0.85, 0.94) and specificity of 0.74 (95% CI: 0.65, 0.81). The pooled sensitivity for mechanical sensitivity (palpation) was 0.88 (95% CI: 0.78, 0.95), and specificity was 0.61 (95% CI: 0.50, 0.71). Conclusion: Limited studies are available that discuss the clinical diagnosis of CZP, and significant heterogeneity is present in the available data. In this review, intersegmental mobility tests were found to be the most accurate. Clustering of tests, agreement on a reference standard, and further exploration of CZP referral patterns are recommended.
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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 | 0.013 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.024 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".