Could immunotherapy be a hope for addiction treatment?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Could immunotherapy be a hope for addiction treatment?Substance use disorder (SUD) or addiction is defined as a chronic illness in which there is physical and psychological dependence on psychoactive substances.It is characterized by compulsive drug-seeking behavior, lack of self-control during use, and negative physiological and psychological changes (e.g., irritability, anxiety, and dysphoria) in the absence of the substance. 1,2ccording to the World Drug Report 2023, it is estimated that 296 million people used psychoactive substances in 2021 and approximately 40 million have developed substance use disorder. 3 Moreover, the number of deaths resulting from psychoactive substance misuse reached around 500.000 in 2019.Finally, even if it does not lead to death, in 2021, the use of drugs generated a "loss of healthy life" of approximately 32 million years. 3 Despite the deleterious effects of drug use being widely known, the prevalence of people who use drugs remains high, which is intrinsically related to the mechanism of action of drugs of abuse.According to DSM-5, psychoactive substances encompass ten distinct classes of drugs: stimulants, caffeine, alcohol, tobacco, marihuana, opioids, anxiolytics, sedatives and hypnotics, inhalants, hallucinogens, and other unknown substances. 1 Despite being divided into different categories and presenting various neuropharmacological properties, the psychoactive substances act directly on the reward system, 4 which is formed mainly by the Ventral Tegmental Area (VTA), the Nucleus Accumbens (NAc) and the Prefrontal Cortex (PFC), 5 promoting an imbalance in the levels of neurotransmitters in the mesocorticolimbic dopaminergic and in the corticolimbic glutamatergic pathways. 2,6,7Consequently, psychoactive substances reorganize and promote plastic changes in these circuits of the Central Nervous System (CNS), "hijacking" neural adaptive motiva-
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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 | 0.001 |
| 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 | 0.001 | 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 it