Is it time to consider comorbid substance abuse as a new indication for antipsychotic drug development?
Bibliographic record
Abstract
Comorbid drug abuse in schizophrenia has been consistently reported as high, with estimates ranging between 10-70%. Comorbid addictive states in schizophrenia are possibly multifactorial, yet recent research assigns a significant neurobiological role in its genesis. Abnormalities in hippocampal/cortical function in schizophrenia which mediate reward and reinforcement behavior are identified as central to the development and maintenance of comorbid addictive states. Preliminary data suggest that the vulnerability of patients with schizophrenia to substance use disorders may be a primary disease symptom. The management of comorbid substance abuse in schizophrenia relies on the use of antipsychotic medications. Recent data raise the concern about whether first-generation antipsychotics in long-term use can conversely lead to enhancement of the abused substance's reinforcing properties. Some recent reports have assigned a favorable outcome to clozapine and second-generation antipsychotics, pointing to a possible differential role for various antipsychotics. In view of the high prevalence of comorbid drug abuse in schizophrenia, its impact on outcome of treatment and the recent emerging neurobiological information, it is my contention that comorbid drug abuse constitutes a dimension by itself and deserves to receive an indication in the development of new antipsychotics similar to negative symptoms or cognitive deficits.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.012 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".