Cognitive improvement among abstinent methamphetamine users: A 2‐year prospective longitudinal study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The adverse impact of chronic methamphetamine (MA) use on cognitive function has been described in previous studies, but limited evidence is available for abstinent users from prospective longitudinal studies. The aim of the present study was to assess cognitive function of varying abstinent duration. METHODS: This prospective longitudinal study was conducted with baseline and four follow-up interviews every 6 months over 2 years in 358 MA users in Guangdong province, China. The Montreal Cognitive Assessment (MoCA) was used to measure cognitive function. Generalized estimating equation (GEE) analysis was used to examine within-subjects relationships between abstinence and cognitive consequences over time. RESULTS: The repeated measure analysis of variance showed significant differences in the total MoCA score and all subscale scores (except Orientation) in the 24 months follow-up. The GEE model showed that abstinence from MA in the past 6 months predicted an increase of 0.66 (95% confidence interval [CI] = 0.29 to 1.05, p = .002) in MoCA score changes compared with the nonabstinence MA users. Abstinence in the past 12, 18, and 24 months predicted an increase in MoCA total score changes of 1.25 (95% CI = -0.23 to 2.74), 2.15 (95% CI = -0.79 to 5.09), and 5.28 (95% CI = -2.01 to 12.58), respectively, but none of these was statistically significant. DISCUSSION AND CONCLUSIONS: Cognitive function was potentially improved following 6 months of MA abstinence. SCIENTIFIC SIGNIFICANCE: This study extends prior research by long-term follow-up in big sample MA abstinence users. Findings from study support the need for a comprehensive measure to decrease MA use and promote the recovery of cognitive impairment.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".