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WHAT INTEGRATED INTERDISCIPLINARY AND TRANSLATIONAL RESEARCH MAY TELL US ABOUT ADDICTION

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

Bibliographic record

VenueAddiction · 2010
Typeletter
Languageen
FieldNeuroscience
TopicNeurotransmitter Receptor Influence on Behavior
Canadian institutionsnot available
FundersNational 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
KeywordsAddictionPsychologyNeurocognitiveBehavioral addictionSubstance dependenceClinical psychologyPsychiatryCognition

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0020.011
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.062
GPT teacher head0.359
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2010
Admission routes1
Has abstractyes

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