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Record W3111625195 · doi:10.5539/jmbr.v10n1p185

The Association between Sex Hormones and Developmental Stuttering Disorder: A Systematic Review

2020· review· en· W3111625195 on OpenAlexvenueno aff
Hossein Shayeste Yekta, Farya Fakoori, Hiwa Mohammadi, Siavash Vaziri

Bibliographic record

VenueJournal of Molecular Biology Research · 2020
Typereview
Languageen
FieldPsychology
TopicStuttering Research and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsStutteringPsychologyTestosterone (patch)PopulationAssociation (psychology)Developmental psychologyMEDLINEClinical psychologyMedicineInternal medicineBiologyPsychotherapist

Abstract

fetched live from OpenAlex

Stuttering is a male-biased speech motor control disorder that lead to disruption in the rhythm of speech. The effect of sex on development of stuttering is well known; males are more susceptible to and less recovered from stuttering than female. Sex hormones have been studied as a main accused factor for this gender dependency of the disorder. The aim of this systematic review is to navigate the extent of previous research about the relationship of developmental stuttering and sex hormones. Toward these ends, a comprehensive, electronic review of past concepts regarding the relationship of stuttering with sex hormones and digit ratio as an indirect index for fetal testosterone exposure, in Scopus, Science Direct, PubMed, Medline, Embase, and Cochrane database was carried out to identify potential studies for the review. Inclusion criteria were original quantitative research, written in English, used human subjects and published from 2000 through 2020. Findings were mixed, although potential patterns were identified. There were methodological limitations such as small participant numbers, in the targeted population in this review research. The findings from this current study add to the growing body of evidence demonstrating that sex hormone have a significant association with stuttering.

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)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.845
Threshold uncertainty score0.669

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.101
GPT teacher head0.482
Teacher spread0.381 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations1
Published2020
Admission routes1
Has abstractyes

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