Evaluation of Stuttering in Multilingual Speakers: A Systematic Review of Diagnostic Recommendations
Bibliographic record
Abstract
INTRODUCTION: There is no definitive, comprehensive guide for diagnosing stuttering in multilingual speakers, and research suggests that monolingual-based diagnostic criteria may lead to misidentification in this population. This systematic review aimed to identify and consolidate conventional diagnostic guidelines for multilingual speakers and evaluate their validity in light of empirical evidence on stuttering and multilingualism. METHOD: A systematic review was conducted using PubMed, ScienceDirect, SAGE, CINAHL, and Google Scholar using specific MESH terms (e.g., "stuttering," "multilingual," "bilingual," "bilingualism," "assessment," "diagnosis", or "evaluation"). No limits were placed on the years of publication. A hand search of the bibliography of articles that met the criteria was also performed to identify articles that were not found by the database search. RESULTS: After removing duplicates and applying the exclusion and inclusion criteria, 53 articles remained. Recommendations were related to the nature of speech disfluencies (e.g., evaluate types and frequencies of stuttering behaviors and associated behaviors, such as excessive muscular tension and physical concomitants in all languages spoken), diagnostic procedures (e.g., consider case history; language history, proficiency, and dominance), and supplementary data (e.g., use standardized assessments designed for multilingual speakers). CONCLUSION: Collectively, findings support a holistic approach that considers current knowledge about multilingual language development, speech patterns, and broader speaker characteristics beyond disfluencies. In particular, there was an overwhelming consensus that stuttering should be evaluated in all languages spoken, and that the presence of dysrhythmic phonation (i.e., blocks and prolongations) and associated behaviors, such as excessive tension and physical concomitants (e.g., eye twitching, facial grimacing, head jerks), increase diagnostic accuracy. Results also highlight the need for comprehensive, validated guidelines, and dynamic assessments (e.g., comparing test and retest performance) to augment standardized evaluation for multilingual speakers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".