Bibliographic record
Abstract
Synaptic plasticity is defined as the modification of the transmission of synapses. It has been proven to be strongly associated with learning. Thus, drug-evoked synaptic plasticity in brain reward circuits can establish persistent learning of addictive drugs, reflecting the neural basis underlying addiction. The mesolimbic dopamine pathway has been widely indicated to be strongly associated with opioid use disorder (OUD) and other drug addictions. The paper focuses on discussing the drug-evoked neural plasticity underlying two important stages called intoxication and withdrawal which are critical for addiction and reinstatement of drug use respectively. The paper first explores the neural basis of OUD, emphasizing drug-induced plasticity at glutamate and Gamma-aminobutyric acid (GABA) synapses on neurons of key substrates in the pathway and how they influence mesolimbic dopamine (DA) neuron transmission. Then, the review discussed withdrawal-induced neuroplasticity and reorganization of associated neuron circuits, which explain deficits led by withdrawal from opioid administration. An overall understanding of drug-evoked synaptic plasticity in key brain circuits in the development of addiction helps find possible therapeutic methods to prevent the initiation of OUD and reinstatement.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".