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Enregistrement W2136404454 · doi:10.1111/j.1360-0443.2009.02817.x

WHAT INTEGRATED INTERDISCIPLINARY AND TRANSLATIONAL RESEARCH MAY TELL US ABOUT ADDICTION

2010· letter· en· W2136404454 sur OpenAlexfundno aff
Marc N. Potenza

Notice bibliographique

RevueAddiction · 2010
Typeletter
Langueen
DomaineNeuroscience
ThématiqueNeurotransmitter Receptor Influence on Behavior
Établissements canadiensnon disponible
Organismes subventionnairesNational Institute of Dental and Craniofacial ResearchNational Institute on Drug AbuseNational Institute on Alcohol Abuse and AlcoholismNational Institutes of HealthRoyal Society of CanadaNational Center for Responsible Gaming
Mots-clésAddictionPsychologyNeurocognitiveBehavioral addictionSubstance dependenceClinical psychologyPsychiatryCognition

Résumé

récupéré en direct d'OpenAlex

In his paper [1], Kalant raises important points regarding how addiction is conceptualized and researched. This topic seems particularly timely, given current preparations for the next editions of the International Classification of Diseases and Diagnostic and Statistical Manual (DSM). At the paper's onset, Kalant provides a definition for addiction: ‘compulsive use of the drug despite the occurrence of adverse consequences’. Although this definition was widely agreed upon in prior iterations of the DSM including the current DSM-IV-TR [2], several DSM-V research work-groups (including those relating to substance use disorders and obsessive-compulsive spectrum disorders) have recently discussed at length the extent to which non-substance behaviors or disorders (e.g. related to gambling) should be considered addictions [3]. Kalant cites the importance to studies of addiction of the decision to self-administer drug (or by extension engage in the potentially addictive behavior) by individuals who become addicted. Decision-making represents an important and arguably central component of addiction [4], and performance of individuals with addictions on neurocognitive tasks assessing decision-making has been associated with treatment outcome [5] and real-life measures such as the ability to maintain employment [6]. Precisely how decision-making relates to addictions, however, is less clear. For example, individual differences in decision-making prior to substance exposure could lead to initial engagement in substance use and substance use may generate suboptimal decision-making. Animal models seem particularly well suited to investigate such questions, and pre-clinical studies indicate that not only does substance-naive impulsive decision-making predict substance self-administration [7,8], but also that substance intake, probably in a developmentally sensitive fashion, influences decision-making [9]. Decision-making and related processes (e.g. impulsivity) represent complex, multi-faceted constructs [10], with their expression influenced by genetic and environmental factors in a dynamic and complicated fashion. Multiple individual differences, including those relating to gender, emotional reactivity and stress responsiveness, among others, represent important considerations with respect to addictions and frequently co-occurring disorders, and an improved understanding of how individual differences relate to decision-making, and addiction will probably be facilitated by an integrative translational research approach involving multiple disciplines [11]. Such approaches ideally might involve studying behaviors (e.g. through analogous tasks in pre-clinical and clinical settings) and using assessments (e.g. relating to brain imaging or genetics) across species such that findings from each study might be linked directly with one another, while at the same time affording unique insight through the utilization of ‘species-specific’ techniques (e.g. through self-report, diagnostic and real-life measures in human studies and genetic manipulation and neurochemical measurements from brain tissues in pre-clinical studies) [12]. Currently I have the privilege of participating in an interdisciplinary research consortium on stress, self-control and addiction (http://stress.yale.edu/ and http://stress.yale.edu/projects.html). The consortium includes 14 coordinated and integrated research projects that involve rats, non-human primates and humans and use molecular/cellular, genetic, brain imaging, behavioral, clinical and epidemiological approaches. While there exist organizational and logistical challenges in conducting interdisciplinary team science, such an approach holds significant promise for understanding complex neuropsychiatric conditions such as addiction that are currently frequently refractory to existing treatments [13]. Even if such studies do not identify the cause of addiction [14], they have tremendous potential for generating significant advances in prevention and treatment strategies and reducing the suffering and societal burden associated currently with addictions. Dr Potenza consults for and is an advisor to Boehringer Ingelheim; has consulted for and has financial interests in Somaxon; has received research support related to the gambling industry (Mohegan Sun Casino and the National Center for Responsible Gaming and its Institute for Research on Gambling Disorders) and pharmaceutical industry (Forest, Ortho-McNeil, Oy-Control/Biotie, Glaxo-SmithKline); and has performed legal consulting in issues related to impulse control disorders and addictions. This study was funded in part by NIH grants R01 DA019039, R01 DA020908, R01 DA020709, RL1 AA017539, P50 DA09241, P50 DA016556, R37 DA15969, P01 DA022446 and UL1 DE19586, the NIH Roadmap for Medical Research/Common Fund, the VA VISN1 MIRECC, Women's Health Research at Yale, and a Center of Research Excellence Grant from the National Center for Responsible Gaming. Its contents are solely the responsibility of the author and do not necessarily represent the official views of any of the funding agencies.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Intégrité de la recherche, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesIntégrité de la recherche
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,271
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0010,002
Science ouverte0,0010,000
Intégrité de la recherche0,0020,011
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,062
Tête enseignante GPT0,359
Écart entre enseignants0,297 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations6
Publié2010
Routes d'admission1
Résumé présentoui

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