Nerve Decompression in Occipital Neuralgia: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Occipital neuralgia (ON) is a debilitating craniofacial pain disorder often refractory to conservative treatments. Nerve decompression surgery has emerged as a promising intervention, yet the long-term efficacy and optimal patient selection remain unclear. This systematic review and meta-analysis evaluate the effectiveness of occipital nerve decompression in reducing pain frequency, intensity, and duration in patients with ON. A systematic search of PubMed, Embase, and Scopus was conducted following PRISMA guidelines. Studies evaluating greater occipital nerve (GON) or lesser occipital nerve (LON) decompression for ON were included. Risk of bias was assessed using the Newcastle-Ottawa Scale (NOS) for cohort studies and the Joanna Briggs Institute (JBI) checklist for case series and observational studies. A random-effects meta-analysis estimated pooled effects on pain reduction, and heterogeneity was analyzed using I² statistics. Twelve studies comprising 838 patients were included. Meta-analysis demonstrated a significant reduction in pain frequency by 20.30 days/month (95% CI: 16.53-24.08, P<0.0001) following nerve decompression. Subgroup analysis revealed superior outcomes for post-traumatic ON, while chronic migraine-related ON showed more variability. Technique modifications, such as midline versus separate incisions for LON decompression, influenced reoperation rates (4.4% versus 15.2%, P<0.05). Heterogeneity was high (I²=97.21%), likely due to surgical variability and patient selection differences. Nerve decompression significantly reduces ON-related pain, though patient selection and surgical technique optimization remain crucial. Standardized protocols and prospective trials are needed to refine clinical guidelines.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.035 |
| Bibliometrics | 0.006 | 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) | 0.004 | 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".