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Record W4393200889 · doi:10.1080/17549507.2024.2327472

Contemporary clinical conversations about stuttering: What does brain imaging research mean to clinicians?

2024· article· en· W4393200889 on OpenAlexaff
Soo‐Eun Chang, Eric S. Jackson, Gissella Santayana, Gillian Zavos, Mark Onslow

Bibliographic record

VenueInternational Journal of Speech-Language Pathology · 2024
Typearticle
Languageen
FieldPsychology
TopicStuttering Research and Treatment
Canadian institutionsThe Speech and Stuttering Institute
FundersNational Institute on Deafness and Other Communication Disorders
KeywordsStutteringPsychologyNeuroimagingClinical neuroscienceCognitive scienceNeuroscienceCognitive psychologyDevelopmental psychologyNeurology

Abstract

fetched live from OpenAlex

PURPOSE: To discuss among neuroscientists and community speech-language pathologists what brain imaging research means to clinicians. METHOD: Two university neuroscientists and two speech-language pathologists in private practice discussed the matter. Written conversational turns in an exchange were limited to 100 words each. When that written dialogue was concluded, each participant provided 200 words of final reflection about the matter. RESULT: For now, neuroscience treatments are not available for clinicians to use. But sometime in the future, a critical mass of neuroscientists will likely produce such treatments. The neuroscientists expressed diverse views about the methods that might be used for that to occur. CONCLUSION: Neuroscience does have practical clinical application at present and, in a way, that does not exclude a concurrent influence of the social model of disability. As such, the current practices of clinicians are supported by basic neuroscience research.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.718
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.124
GPT teacher head0.539
Teacher spread0.414 · 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; both teacher heads agree on what is shown here.

Study designOther design
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

Citations4
Published2024
Admission routes1
Has abstractyes

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