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Record W2339074466 · doi:10.1097/ypg.0000000000000108

Possible association between the prolactin receptor gene and callous-unemotional traits among aggressive children

2015· article· en· W2339074466 on OpenAlexafffund
Yuko Hirata, Clement C. Zai, Behdin Nowrouzi‐Kia, Sajid A. Shaikh, James L. Kennedy, J.H. Beitchman

Bibliographic record

VenuePsychiatric Genetics · 2015
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersEli Lilly CanadaCentre for Addiction and Mental Health FoundationCentre for Addiction and Mental HealthAmerican Foundation for Suicide Prevention
KeywordsAggressionProlactinGenotypeAssociation (psychology)Prolactin receptorGeneInternal medicinePsychopathyEndocrinologyPsychologyReceptorGeneticsBiologyMedicineDevelopmental psychologyPersonalityHormone

Abstract

fetched live from OpenAlex

This study examined the possible association between prolactin (PRL) system genes and callous-unemotional (CU) traits in childhood-onset aggression. Two markers for the PRL peptide gene and three markers for the prolactin receptor (PRLR) gene were genotyped. The participants were assessed on the CU subscale using five items from the Antisocial Process Screening Device. Genotype analysis showed nominally significant results with PRLR_rs187490 (uncorrected P=0.01), with the GG genotype associated with higher CU scores. This is the first paper to evaluate the relationship of PRL system genes with CU traits in childhood-onset aggression.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.279
Teacher spread0.256 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2015
Admission routes2
Has abstractyes

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