Impact of tramadol and heroin abuse on electroencephalography structure and cognitive functions
Bibliographic record
Abstract
Abstract Background Opioids, defined as medicines that stimulate opioid receptors, are primarily used in the treatment of moderate to severe pain. They induce central nervous system (CNS) adverse effects. This study aimed to assess the effect of opioids on brain electrical activity, the effect of opioids on cognitive functions, and corroborate whether there was any correlation between changes in brain electrical activity and cognitive functions that may do in opioid addicts. Methods This cross-sectional case–control study was performed on 80 cases (divided into two groups 40 cases with tramadol use disorders and 40 cases with heroin use disorders) and 40 age-/sex-matched healthy control. All subjects were subordinated to neuropsychiatric evaluation, assessment of opioid use complaint through history from the case and his relatives, substance monitoring in urine, medicine abuse screening test (DAST), electroencephalography (EEG), and cognitive assessment by Montreal Cognitive Assessment (MOCA). Results Opioid dependence convinced global cognitive function impairment, specific cognitive disciplines impairment that included visual-conceptual, visual-motor tracking, visual-constructional skills, language function, attention, memory, and orientation. Additionally, affection of the brain’s electrical activities with significant changes compared with control. Comparison of cognitive impairment substantiated by lower cognitive scores in relation to abnormal EEG changes among studied case groups revealed significant differences. Conclusions Opioid abusers had a significant impairment of cognitive functions and EEG changes with a significant correlation between changes in brain electrical activity and impairment of cognitive functions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".