Evaluating the Impact of Naltrexone on the Rat Gambling Task to Test Its Predictive Validity for Gambling Disorder
Bibliographic record
Abstract
Gambling Disorder has serious consequences and no medications are currently approved for the treatment of this disorder. One factor that may make medication development difficult is the lack of animal models of gambling that would allow for the pre-clinical screening of efficacy. Despite this, there is evidence from clinical trials that opiate antagonists, in particular naltrexone, may be useful in treating gambling disorder. To-date, the effects of naltrexone on pre-clinical models of gambling have not been evaluated. The purpose of the present study was to evaluate the effects of naltrexone in an animal model of gambling, the rat gambling task (rGT), to determine whether this model has some predictive validity. The rGT is a model in which rats are given a choice of making either a response that produces a large reward or a small reward. The larger the reward, the greater the punishment, and thus this task requires that the animal inhibit the 'tempting' choice, as the smaller reward option produces overall the most number of rewards per session. People with gambling disorder chose the tempting option more, thus the rGT may provide a model of problem gambling. It was found that naltrexone improved performance on this task in a subset of animals that chose the 'tempting', disadvantageous choice, more at baseline. Thus, the results of this study suggest that the rGT should be further investigated as a pre-clinical model of gambling disorder and that further investigation into whether opioid antagonists are effective in treating Gambling Disorder may be warranted.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".