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Record W2599312461 · doi:10.1093/schbul/sbx021.213

155. Tobacco Use and Psychosis Risk in Persons at Clinical High Risk

2017· article· en· W2599312461 on OpenAlexaff
Heather A. Burrell, Michael T. Lawson, Jean Addington, Carrie E. Bearden, Kristin S. Cadenhead, Tyrone D. Cannon, Barbara A. Cornblatt, Clark Jeffries, Daniel H. Mathalon, Thomas H. McGlashan, Larry J. Seidman, Ming T. Tsuang, Elaine F. Walker, Scott W. Woods, Diana O. Perkins

Bibliographic record

VenueSchizophrenia Bulletin · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicHealth, Environment, Cognitive Aging
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsProdromeMedicinePsychosisProspective cohort studyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Background: The purpose of this study was to evaluate the role of tobacco use in the development of psychosis in individuals at clinical high risk. Methods: The North American Prodrome Longitudinal Study is a 2-year multisite prospective case–control study of persons at clinical high risk that aims to better understand predictors and mechanisms for the development of psychosis. The cohort consisted of 764 clinical high risk and 279 healthy comparison subjects. Clinical assessments included tobacco and substance use and several risk factors associated with smoking. Results: Clinical high risk subjects were more likely to smoke cigarettes than unaffected subjects (Light Smoking OR = 3.0, 95%CI = 1.9–5; Heavy Smoking OR = 4.8, 95%CI = 1.7–13.7). In both groups, smoking was associated with substance use, stressful life events, and perceived discrimination and in clinical high risk subjects with childhood emotional neglect and adaption to school. Clinical high risk subjects reported higher rates of several factors previously associated with smoking. After controlling for these factors, the relationship between clinical high risk state and smoking became non-significant (Light Smoking OR = 1.9, 95%CI = 0.7–5.2; Heavy Smoking OR = 0.9, 95%CI = 0.1–7.2). Moreover, baseline smoking status (HR = 1.16, 95%CI = 0.82–1.65) and categorization as ever smoked (HR = 1.3, 95%CI = 0.8–2.1) did not predict time to conversion. Conclusion: Persons at high risk for psychosis are more likely to smoke compared to unaffected persons. Factors associated with smoking in both groups were more common in the clinical high risk cohort, and smoking status in clinical high risk subjects did not predict conversion risk. These findings did not support a causal relationship between smoking and psychosis. Funding: This study was supported by the National Institute of Mental Health (NIMH) [grant U01 MH081984 to J.A.; grants U01 MH081928; P50 MH080272; Commonwealth of Massachusetts SCDMH82101008006 to L.J.S.; grants R01 MH60720, U01 MH082022 and K24 MH76191 to K.S.C.; grant U01 MH081902 to T.D.C.; P50 MH066286 (Prodromal Core) to C.E.B.; grant U01 MH082004 to D.O.P.; grant U01 MH081988 to E.F.W.; grant U01 MH082022 to S.W.W.; and U01 MH081857-05 grant to B.A.C.] and the National Institute of Environmental Health Sciences (NIEHS) grant T32ES007018 to M.T.W. The NIMH and NIEHS had no further role in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.005

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 head0.026
GPT teacher head0.287
Teacher spread0.261 · 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 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

Citations3
Published2017
Admission routes1
Has abstractyes

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