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Record W2168651385 · doi:10.1375/twin.10.4.564

Trauma Exposure and Stress Response: Exploration of Mechanisms of Cause and Effect

2007· article· en· W2168651385 on OpenAlexaff
Kerry L. Jang, Steven Taylor, Murray B. Stein, Shinji Yamagata

Bibliographic record

VenueTwin Research and Human Genetics · 2007
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTwin studyPosttraumatic stressMajor traumaMedicinePsychological resilienceFight-or-flight responseClinical psychologyPsychologyPsychiatryBiologyGeneticsGeneSocial psychologyHeritability

Abstract

fetched live from OpenAlex

People differ markedly in their risk for developing posttraumatic stress symptoms (PTSS) after exposure to traumatic events. Twin studies suggest that the trauma-PTSS relationship is moderated by genetic and environmental influences. The present study tested for specific types of genetic and environmental interaction effects on PTSS. A sample of 222 monozygotic and 184 dizygotic twin pairs reported on lifetime frequency of assaultive and nonassaultive trauma and associated PTSS. Biometric analyses indicated that in the case of nonassaultive trauma, PTSS were directly affected by environmental factors that also influence exposure to nonassaultive trauma. For assaultive trauma both genetic and non-shared environmental influences jointly affected PTSS, and the number of traumatic events moderated the severity of PTSS. Genetic factors were found to become less important beyond some threshold (e.g., 3 or 4 types of serious trauma) suggesting that genetic factors - which may confer either risk or resilience to PTSS - modify these symptoms within a range of human experience, beyond which environmental effects supervene.

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)0.003
metaresearch head score (Gemma)0.007
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 score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.192
GPT teacher head0.468
Teacher spread0.276 · 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

Citations28
Published2007
Admission routes1
Has abstractyes

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