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Record W2139854474 · doi:10.1503/jpn.090177

White-matter abnormalities in adolescents with long-term inhalant and cannabis use: a diffusion magnetic resonance imaging study

2010· article· en· W2139854474 on OpenAlexvenueno aff
Murat Yücel

Bibliographic record

VenueJournal of Psychiatry and Neuroscience · 2010
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsnot available
FundersNational Health and Medical Research CouncilMedical Research Council
KeywordsWhite matterIntoxicative inhalantPsychosocialCannabisFractional anisotropyDiffusion MRIPsychiatryPsychologyMedicinePediatricsMagnetic resonance imagingClinical psychologyRadiology

Abstract

fetched live from OpenAlex

BACKGROUND: There is growing evidence that inhalants are neurotoxic to white matter, yet limited work has been conducted to investigate the neurobiologic effects of long-term exposure among adolescent users, despite inhalant use being most prominent during this developmental period. METHODS: We used diffusion tensor imaging to examine white-matter integrity in 11 adolescents who used inhalants, 11 matched cannabis users and 8 drug-naive controls. RESULTS: Although both groups of drug users had white-matter abnormalities (i.e., lower fractional anisotropy), abnormalities were more pronounced in the inhalant group, particularly among early-onset users. LIMITATIONS: The findings of this study should be considered in light of its small sample size, cross-sectional design and the complex psychosocial background of long-term inhalant users. CONCLUSION: White-matter abnormalities may underpin long-term behavioural and mental health problems seen in individuals with long-term inhalant use.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.294

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.301
Teacher spread0.284 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations86
Published2010
Admission routes1
Has abstractyes

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