Use of levamisole-adulterated cocaine is associated with increased load of white matter lesions
Bibliographic record
Abstract
Background: Cocaine use has been associated with vascular pathologies, including cerebral white matter hyperintensities. Street cocaine is most often adulterated with levamisole, an anthelminthic drug that may also be associated with vascular toxicity. However, whether levamisole exposure from cocaine consumption further accelerates the development of white matter lesions remains unknown. Methods: We investigated the association of cocaine and levamisole exposure with white matter hyperintensities in 35 chronic cocaine users and 34 healthy controls. We measured cocaine and levamisole concentrations in hair samples, which reflected exposure up to 6 months previously. We assessed the number and total surface area of the white matter hyperintensities using structural MRI (FLAIR sequence). Using generalized linear models, we analyzed the contributions of cocaine and levamisole to the number and area of white matter hyperintensities, accounting for several confounding factors. Results: Analysis using generalized linear models revealed that cocaine users had more white matter hyperintensities in terms of total surface area, but not in terms of number. Further generalized linear models that included cocaine and levamisole hair concentrations (instead of group) as predictors indicated that levamisole exposure was strongly associated with more and larger white matter hyperintensities, suggesting that the elevated white matter hyperintensities in cocaine users were driven mainly by levamisole exposure. Finally, white matter hyperintensities in levamisole-exposed cocaine users were located primarily in the periventricular and juxtacortical white matter. Limitations: The sample size was moderate, and blood pressure was not systematically assessed. Conclusion: As an adulterant of cocaine, levamisole appears to increase the risk of white matter injury.
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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 | 0.000 | 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".