Understanding polydrug use: review of heroin and cocaine co‐use
Bibliographic record
Abstract
The use of cocaine by heroin-dependent individuals, or by patients in methadone or buprenorphine maintenance treatment, is substantial and has negative consequences on health, social adjustment and outcome of opioid-addiction treatment. The pharmacological reasons for cocaine use in opioid-dependent individuals, however, are poorly understood and little is known about the patterns of heroin and cocaine co-use. We reviewed anecdotal evidence suggesting that cocaine is co-used with opioid drugs in a variety of different patterns, to achieve different goals. Clinical and preclinical experimental evidence indicates that the simultaneous administration of cocaine and heroin (i.e. 'speedball') does not induce a novel set of subjective effects, nor is it more reinforcing than either drug alone, especially when the doses of heroin and cocaine are high. There is mixed evidence that the subjective effects of cocaine are enhanced in individuals dependent on opioids, although it is clear that cocaine can alleviate the severity of symptoms of withdrawal from opioids. We also reviewed preclinical studies investigating possible neurobiological interactions between opioids and cocaine, but the results of these studies have been difficult to interpret mainly because the neurochemical mechanisms mediating the motivational effects of cocaine are modified by dependence on, and withdrawal from, opioid drugs. Our analysis encourages further systematic investigation of cocaine use patterns among opioid-dependent individuals and in laboratory animals. Once clearly identified, pharmacological and neuroanatomical methods can be employed in self-administering laboratory animals to uncover the neurobiological correlates of specific patterns of co-use.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".