Revisiting the ‘self-medication’ hypothesis in light of the new data linking low striatal dopamine to comorbid addictive behavior
Bibliographic record
Abstract
Persons with schizophrenia are at a high risk, almost 4.6 times more likely, of having drug abuse problems than persons without psychiatric illness. Among the influential proposals to explain such a high comorbidity rate, the 'self-medication hypothesis' proposed that persons with schizophrenia take to drugs in an effort to cope with the illness and medication side effects. In support of the self-medication hypothesis, data from our earlier clinical study confirmed the strong association between neuroleptic dysphoria and negative subjective responses and comorbid drug abuse. Though dopamine has been consistently suspected as one of the major culprits for the development of neuroleptic dysphoria, it is only recently our neuroimaging studies correlated the emergence of neuroleptic dysphoria to the low level of striatal dopamine functioning. Similarly, more evidence has recently emerged linking low striatal dopamine with the development of vulnerability for drug addictive states in schizophrenia. The convergence of evidence from both the dysphoria and comorbidity research, implicating the role of low striatal dopamine in both conditions, has led us to propose that the person with schizophrenia who develops dysphoria and comorbid addictive disorder is likely to be one and the same.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".