Cognitive Impairment and Tramadol Dependence
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Cognitive impairment is one of the consequences of substance abuse. Tramadol abuse is a public health problem in Egypt. The objective of this study was to estimate the prevalence and correlates of cognitive impairment among tramadol-abuse patients and control subjects. METHODS: This study included 100 patients with tramadol abuse and 100 control subjects (matched for age, sex, and education) who were recruited from Zagazig University Hospital, Egypt. Patients were divided into 2 groups: patients who used tramadol only (tramadol-alone group) and patients who used tramadol and other substances (polysubstance group). The participants were interviewed using Montreal Cognitive Assessment test and had urine screening for drugs. RESULTS: Twenty-four percent of the cases used tramadol alone, whereas the remaining used tramadol and other substances, mainly cannabis (66%) and benzodiazepines (27%). Tramadol-abuse patients were about 3 times more likely to have cognitive impairment than control subjects (81% vs 28%). Tramadol-alone patients were more than 2 times more likely to have cognitive impairment than control subjects (67% vs 28%). Cognitive impairment was significantly associated with polysubstance abuse. There was no association between cognitive impairment and sociodemographic or clinical factors. CONCLUSIONS: Cognitive impairment occurs commonly among tramadol-abuse patients. Memory impairment is the most common cognitive domain to be affected. There is a significant association between cognitive impairment and polysubstance abuse.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".