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Record W4386471102 · doi:10.1044/2023_jslhr-23-00070

Predictors of Functional Communication Outcomes in Children With Idiopathic Motor Speech Disorders

2023· article· en· W4386471102 on OpenAlexaff
Aravind Kumar Namasivayam, Hyunji Shin, Rosane Nisenbaum, Margit Pukonen, Pascal van Lieshout

Bibliographic record

VenueJournal of Speech Language and Hearing Research · 2023
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsToronto Rehabilitation InstituteThe Speech and Stuttering InstituteSt. Michael's HospitalOccupational Cancer Research CentrePublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsAudiologyOdds ratioPsychologyRating scalePopulationIntervention (counseling)OddsLogistic regressionMedicineDevelopmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of the study was to investigate child- and intervention-level factors that predict improvements in functional communication outcomes in children with motor-based speech sound disorders. METHOD: = 48) participated. Multivariable logistic regression models estimated odds ratios and 95% confidence intervals for the association between minimal clinically important difference in the Focus on the Outcomes of Communication Under Six scores and multiple child-level (e.g., age, sex, speech intelligibility, Kaufman Speech Praxis Test diagnostic rating scale) and intervention-level predictors (dose frequency and home practice duration). RESULTS: Overall, 65% of participants demonstrated minimal clinically important difference changes in the functional communication outcomes. Kaufman Speech Praxis Test rating scale was significantly associated with higher odds of noticeable change in functional communication outcomes in children. There is some evidence that delivering the intervention for 2 times per week for 10 weeks provides benefit. CONCLUSION: A rating scale based on task complexity has the potential for serving as a screening tool to triage children for intervention from waitlist and/or determining service delivery for this population.

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)0.002
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 score0.017
Threshold uncertainty score0.287

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.040
GPT teacher head0.357
Teacher spread0.318 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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