Tramadol and the occurrence of seizures: a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction: Tramadol is a synthetic opioid which is commonly used around the world to relieve moderate to severe pain. One of the serious possible complications of its use is seizures. The present study aims to investigate and summarize the studies related to tramadol and occurrences of seizures after tramadol use and factors influencing these seizures.Methodology: Our systematic review is compliant with PRISMA guidelines. Two researchers systematically searched PubMed/Medline, Web of Sciences, and Scopus. Cohort, case-control, cross-sectional studies, and clinical trials. The risk of bias was assessed using the Newcastle–Ottawa Scale After article quality assessment, a fixed or random model, as appropriate, was used to pool the results in a meta-analysis. Heterogeneity between the studies was assessed with using I-square and Q-test. Forest plots demonstrating the point and pooled estimates were drawn.Results: A total of 51 articles with total sample size of 101 770 patients were included. The results showed that seizure event rate in the subgroups of tramadol poisoning, therapeutic dosage of tramadol, and tramadol abusers was 38% (95% CI: 27–49%), 3% (95% CI: 2–3%), 37% (95% CI: 12–62%), respectively. Tramadol dose was significantly higher in the patients with seizures than those without (mean differences: 0.82, CI 95%: 0.17–1.46). The odds for occurrence of seizures were significantly associated with male gender (pooled OR: 2.24, CI 95%: 1.80–2.77). Naloxone administration was not associated to the occurrence of seizures (pooled OR: 0.47, 95% CI: 0.15–1.49).Conclusions: Our results demonstrate that the occurrence of seizures in patients exposed to tramadol are dose-dependent and related to male gender, but not related to naloxone administration. Given that, most of the evidence derives from studies utilizing a cross-sectional design, the association of tramadol with seizures should not be considered to be definitively established
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.041 |
| Bibliometrics | 0.009 | 0.010 |
| 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".