Hypoxic brain damage in Methadone misuse: insights from MRI imaging and comparative study
Bibliographic record
Abstract
Abstract Background: This study aimed to investigate the potential presence of brain disorders, particularly hypoxia, via magnetic resonance imaging (MRI) in patients misusing methadone, with a comparison to regular opium users and a control group. Methods: Conducted as a cross-sectional comparative study at Kamali Hospital in Karaj, Iran, the research included male participants comprising methadone users, opium users, and controls. Inclusion criteria were stringent, focusing on substance use duration and absence of brain structural disorders. MRI scans were performed using a 1.5T MRI scanner. Qualitative MRI assessments and chi-square tests analyzed associations between substance use and hypoxia, while logistic regression examined potential confounding variables. Results: Significant hypoxia was observed in the methadone group (16.7%, 5/24; p = 0.00057), with no cases in the opium or control groups. Logistic regression analysis showed no significant predictors of hypoxia regarding dose, duration of use, or age. MRI findings in methadone users with hypoxia included varied ADC intensities, high signal intensities on T2-weighted and diffusion-weighted imaging (DWI) sequences, and angiogenesis patterns on TOF sequences. The co-use of methadone and alcohol was noted in three of the five hypoxia cases. Conclusion: Methadone misuse, particularly with alcohol, poses a significant risk of hypoxia, detectable via MRI. This study underscores the need for routine MRI monitoring, stricter regulation of non-prescribed methadone, and enhanced public health education to mitigate misuse risks. Future research should expand sample sizes and incorporate advanced imaging techniques to further elucidate methadone's neurological impact.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".