Neuroimaging Correlates of Internet Gaming Disorder: Can We Achieve the Promise of Translating Understanding of Brain Functioning Into Clinical Advances?
Bibliographic record
Abstract
ABSTRACT Objectives: Here, we aimed to consider the neural factors associated with internet gaming disorder (IGD), as well as the associations between these factors and existing treatments for the disorder. Methods: A narrative review was conducted. Results: Pharmacological as well as psychological treatments for IGD may be associated with specific changes in multiple brain areas and circuits. In particular, frontostriatal and subcortical regions and pathways appear relevant to IGD and its treatment. Conclusions: Neuroimaging holds promise for identifying specific mechanisms underlying IGD interventions. However, to date, firm conclusions are difficult to draw and more research examining neural mechanisms of empirically supported treatments for IGD is needed. Objectifs: Nous avons cherché à examiner les facteurs neuronaux associés au trouble du jeu sur internet (TJI), ainsi que les associations entre ces facteurs et les traitements existants pour ce trouble. Méthodes: Une étude narrative a été réalisée. Résultats: Les traitements pharmacologiques et psychologiques du TJI peuvent être associés à des changements spécifiques dans de multiples zones et circuits cérébraux. En particulier, les régions et voies fronto-striatales et sous-corticales semblent pertinentes pour le TJI et son traitement. Conclusions: La neuro-imagerie est prometteuse pour l’identification des mécanismes spécifiques qui sous-tendent les interventions du TJI. Cependant, à ce jour, il est difficile de tirer des conclusions définitives et il est nécessaire de poursuivre les recherches sur les mécanismes neuronaux des traitements empiriques du TJI.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| 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".