Predictors of cognitive impairment in patients with substance use disorder in Kiambu County, Kenya
Bibliographic record
Abstract
Cognitive impairments induced by substance use contribute to poorer treatment outcomes among patients with substance use disorder (SUD). Neuropsychological assessments are often neglected during patient evaluation in SUD treatment programs owing to the fact that they require extensive time for evaluation and are resource intensive. This inattention is likely to compromise comprehensive treatments which would offer better prognosis for such patients undergoing treatment for SUD. The main objective of this study was to determine predictors of neuro cognitive disorders (NCD) in patients with substance use disorders enrolled in rehabilitation centers in Kiambu County, Kenya. A cross-sectional design was adopted and data collected between Oct-23 to Jan 2024, covering a total of 250 patients aged 18-65 years that consented to participate in the study. Consecutive non-probability sampling technique was deployed in the recruitment of the respondents into the study. A self-rated questionnaire was developed for data collection whereas the Montreal Cognitive Assessment (MoCA) Tool was employed in the screening for cognitive impairment. The prevalence of cognitive impairment was 34.8% (Prevalence per primary substance showed alcohol=37%, .cannabis=22%, and khat=22%). Age Coefficient=0.0852, P=0.013 CI= 0.018- 0.152), education (Coefficient=0.0783, P<0.008 CI= 0.021-0.139), and anxiety disorder (Coefficient=0.4286, P<0.001 CI= 0.317- .540) were found to be significantly associated with neurocognitive disorders at multivariate analysis. This shows that it is important to screen for cognitive impairments during early treatment stages considering the high prevalence rate. This will enhance the choice of treatment course and maximize on treatment outcomes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".