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Record W2900907575 · doi:10.1017/thg.2018.61

Age, Sex, and Genetic and Environmental Effects on Unintentional Injuries in Young and Adult Twins

2018· article· en· W2900907575 on OpenAlexaboutno aff
Simo Salminen, Eero Vuoksimaa, Richard J. Rose, Jaakko Kaprio

Bibliographic record

VenueTwin Research and Human Genetics · 2018
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsnot available
Fundersnot available
KeywordsTwin studyDemographyMedicineYoung adultDizygotic twinsQuarter (Canadian coin)GerontologyHeritabilityBiologyGeneticsObstetricsGeography

Abstract

fetched live from OpenAlex

The aim of this study was to examine the effects of genetic and environment influences and sex on injury involvement using two sets of Finnish twin data. The younger participants were 955 twins born between 1983 and 1987, aged 20 to 24 years. The older participants were 12,428 twins born between 1930 and 1957, aged 33 to 60 years. Within-twin correlations in monozygotic and dizygotic twins suggested that genetic effects play no role in injury involvement among young twins, but do have some effect at older ages. The results indicated that environmental factors have greater importance in injury involvement than genetic factors in the younger twin data set (FT12), whereas in a middle-aged (33-60 years) twin data set, genetic effects explained about quarter of the variance in injury involvement. Sex was a strong contributing factor, with males being generally more prone to injuries than females.

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)0.001
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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.375
Teacher spread0.334 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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