A Systematic Review of Lifestyle Interventions for Neuropathy and Neuropathic Pain: Smoking Cessation
Bibliographic record
Abstract
Background: Neuropathic pain (NP), resulting from damage to the somatosensory nervous system, affects 7–10% of the global population and remains poorly managed despite available therapies. Smoking has been associated with increased pain severity and disease burden, yet its role in neuropathy/NP has not been systematically reviewed. This systematic review synthesizes the existing literature on smoking status and its relationship with neuropathy/NP incidence, prevalence, and severity. Methods: The review was conducted in accordance with PRISMA guidelines and included studies that assessed smoking consumption, dependency, quantity, and cessation in individuals with neuropathy/NP. Summary estimates were stratified by exposure type, and pooled odds ratios and relative risks were calculated. Results: Across 62 studies comprising cohort, case–control, and cross-sectional designs, smoking was consistently associated with greater NP prevalence and pain severity. Smoking dependency was linked to increased incidence, while cessation was associated with reduced risk of NP. Despite considerable heterogeneity and risk of bias, particularly from subjective exposure measurement and inconsistent classification, this relationship remained statistically significant. Conclusions: Findings support the role of smoking as a modifiable risk factor in various etiologies of neuropathy/NP. Cessation may represent a low-cost, low-risk, low-tech adjunctive therapy; however, further robust cessation interventional trials are needed, particularly for less common infectious causes of chronic NP such as leprosy.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".