Auditory Processing and Speech-Sound Disorders
Bibliographic record
Abstract
BACKGROUND: Speech-sound disorders (SSD) have been linked to auditory processing difficulties, and auditory processing disorders (APD) have been related to phonological awareness and literacy development. To this date, there has not been a systematic literature review investigating the results of psychophysiology and language assessments related to SSD and APD in children. METHODS: The literature search was conducted in PubMed, Medline EBSCO, and Scopus to identify studies with children diagnosed/suspected of having APDs and SSDs. The quality of methodology in the selected articles was evaluated with the Newcastle Ottawa Scale. RESULTS: Seven out of 378 relevant studies met the selection criteria. The findings were summarized for children with SSD and APD based on (a) metalinguistic and literacy skills, (b) cognitive abilities, and (c) temporal processing abilities. Three articles indicated that children with APD and SSD exhibit lower temporal task accuracy and reaction time. In two studies, children with SSD exhibited lower scores in discrimination, sequencing, and recall of brief stimuli in rapid succession. CONCLUSIONS: This review revealed associations between SSD severity and APD that may underline low performance in metalinguistic skills. Diagnostic assessments have been proposed based on the review to adequately identify children with SSD and APD and provide useful information for more suitable intervention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".