Spinal manipulation for the management of cervicogenic headache: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Spinal manipulative therapy (SMT) is frequently used to manage cervicogenic headache (CGHA). No meta-analysis has investigated the effectiveness of SMT exclusively for CGHA. OBJECTIVE: To evaluate the effectiveness of SMT for CGHA. DATABASES AND DATA TREATMENT: Five databases identified randomized controlled trials comparing SMT with other manual therapies. The PEDro scale assessed the risk-of-bias. Pain and disability data were extracted and converted to a common scale. A random effects model was used for several follow-up periods. GRADE described the quality of evidence. RESULTS: Seven trials were eligible. At short-term follow-up, there was a significant, small effect favouring SMT for pain intensity (mean difference [MD] -10.88 [95% CI, -17.94, -3.82]) and small effects for pain frequency (standardized mean difference [SMD] -0.35 [95% CI, -0.66, -0.04]). There was no effect for pain duration (SMD - 0.08 [95% CI, -0.47, 0.32]). There was a significant, small effect favouring SMT for disability (MD - 13.31 [95% CI, -18.07, -8.56]). At intermediate follow-up, there was no significant effects for pain intensity (MD - 9.77 [-24.21 to 4.68]) and a significant, small effect favouring SMT for pain frequency (SMD - 0.32 [-0.63 to - 0.00]). At long-term follow-up, there was no significant effects for pain intensity (MD - 0.76 [-5.89 to 4.37]) and for pain frequency (SMD - 0.37 [-0.84 to 0.10]). CONCLUSION: For CGHA, SMT provides small, superior short-term benefits for pain intensity, frequency and disability, but not pain duration, however, high-quality evidence in this field is lacking. The long-term impact is not significant. SIGNIFICANCE: CGHA are a common headache disorder. SMT can be considered an effective treatment modality, with this review suggesting it providing superior, small, short-term effects for pain intensity, frequency and disability when compared with other manual therapies. These findings may help clinicians in practice better understand the treatment effects of SMT alone for CGHA.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.023 |
| Bibliometrics | 0.006 | 0.006 |
| 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) | 0.005 | 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".