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Record W2148690599 · doi:10.1080/1357650x.2015.1102924

The development of hand preference and dichotic language lateralization in males and females with congenital adrenal hyperplasia

2015· article· en· W2148690599 on OpenAlexafffund
Elizabeth Hampson

Bibliographic record

VenueLaterality Asymmetries of Body Brain and Cognition · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSexual Differentiation and Disorders
Canadian institutionsWestern University
FundersMedical Research Council Canada
KeywordsDichotic listeningPsychologyLateralization of brain functionDevelopmental psychologyCongenital adrenal hyperplasiaTestosterone (patch)AudiologyEndocrinologyInternal medicinePhysiologyMedicineNeuroscience

Abstract

fetched live from OpenAlex

People with congenital adrenal hyperplasia (CAH) are exposed to elevated levels of androgens in utero due to overproduction by the adrenal cortex. In order to test whether high levels of prenatal androgens influence the development of handedness preferences and left-hemisphere language representation, we studied 39 patients with CAH (28 females, 11 males) and 25 unaffected sibling controls (17 females, 8 males). Hand preference for 15 unimanual activities was evaluated via questionnaire and pantomime, and a consonant-vowel dichotic listening test was administered. Contemporary theories disagree as to whether high testosterone is said to increase or decrease the probability of developing a typical right-hand preference. Results showed no significant differences on the handedness inventory. Relative to controls, however, patients with CAH showed a significantly larger right-ear advantage on the dichotic syllables task, indicating stronger left-hemisphere lateralization of language. These results support an emerging body of evidence suggesting that high testosterone may bias lateralized development toward the population norm, but run counter to the Geschwind-Behan-Galaburda model which associates high prenatal testosterone with a greater prevalence of left-handedness.

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 score0.055
Threshold uncertainty score0.200

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 head0.026
GPT teacher head0.257
Teacher spread0.232 · 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

Citations8
Published2015
Admission routes2
Has abstractyes

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