Progressive white matter impairment as a predictor of outcome in a cohort of opioid‐dependent patient's post‐detoxification
Bibliographic record
Abstract
White matter impairment is associated with opioid dependence. However, the specific neuropathology related to opioid dependence is still not fully understood. The main aims of this study were to: (1) assess the association between white matter impairment and duration of dependence; (2) examine whether this impairment correlates with treatment outcome measures in opioid-dependent patients post-detoxification. Fifty-eight opioid-dependent patients participated, 20 females and 38 males, across three groups: less than 10 years use (n = 18), 10-15 years use (n = 26) and 16-25+ years use (n = 14). Diffusion tensor imaging was used to assess white matter impairment; whole brain voxel-wise analysis of fractional anisotropy, mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) were performed by Tract-Based-Spatial-Statistics to pinpoint abnormalities in white matter. The longer the subjects were dependent on opioids, the more widespread and severely the white-matter integrity was disrupted. A general linear model was used to examine patients who relapsed compared to those who were abstinent at follow-up. No statistical difference was found between groups (p > 0.05). Partial correlations were performed to investigate the relationship between clinical outcome measures (physical health, psychological well being and quality of life and hope for the future) and white-matter microstructural differences. Significant correlations were found between AD in the posterior corona radiata (L) and MD in the superior longitudinal fasciculus and a clinical measure for HOPE at 9-month follow-up. Nevertheless, it must be noted that the calculation of numerous correlations raises the possibility of a type I error, namely; to incorrectly conclude the occurrence of a significant correlation. The ability to investigate the structure-clinical relationship may improve our understanding of the pathological abnormalities associated with opioid dependence and has promise for use in evaluating future therapeutic outcomes in this population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".