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Record W2652530078 · doi:10.1177/1525740117713165

Evidence-Based Clinical Recommendations for the Administration of the Sequential Motion Rates Task

2017· article· en· W2652530078 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCommunication Disorders Quarterly · 2017
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsToronto Rehabilitation InstituteUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsProtocol (science)Task (project management)Computer scienceHebrewStimulus (psychology)Process (computing)Cognitive psychologySpeech recognitionNatural language processingPsychologyLinguisticsMedicine

Abstract

fetched live from OpenAlex

The sequential motion rates (SMR) task, that involves rapid and accurate repetitions of a syllable sequence, /pataka/, is a commonly used evaluation tool for oro-motor abilities. Although the SMR is a well-known tool, some aspects of its administration protocol are unspecified. We address the following factors and their role in the SMR protocol: (a) selecting the appropriate stimulus for the client—nonword, real word or both, (b) the necessity of a practice round, (c) using visual feedback, (d) using language-specific performance rate norms, and (e) the implications for using different measurements (time-based, rate-based). We also provide rate norms for Hebrew-speaking clients and a pair of simple equations for transforming data from time-based units (seconds) to rate-based units (syllables/s). These recommendations can be considered in the clinical assessment process and may be integrated into the speech-language pathologists’ practice, allowing for a more accurate and cost-effective evaluation procedure.

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.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.822
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.216
GPT teacher head0.435
Teacher spread0.220 · 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