Long Latency Auditory Evoked Responses in the Identification of Children With Central Auditory Processing Disorders: A Scoping Review
Bibliographic record
Abstract
PURPOSE: The long latency auditory evoked responses (LLAERs), originating in the auditory cortex, are often considered a biomarker for maturity in the central auditory system and may therefore be useful in the evaluation of children with central auditory processing disorder (CAPD). However, the characteristics of the LLAERs elicited in this population have not been widely described, and clinical applications remain unclear. The goal of this scoping review was to investigate if LLAERs can be used to identify children with CAPD. METHOD: A systematic search strategy was used to identify studies that analyzed the latencies and amplitudes of P1, N1, P2, and N2 waveforms of the LLAERs. The online databases, including Embase, Web of Science, MEDLINE, PubMed, ProQuest, and CINAHL, as well as the gray literature were searched for papers published in English and French between January 1980 and May 2021. RESULTS: Seventeen papers met the eligibility criteria and were included in the study. Four papers had pre- and posttraining study designs, and the remaining studies were cross-sectional. Several studies reported significant differences in LLAERs between children with CAPD and their normal-hearing peers, and the results tended toward longer latencies and smaller amplitudes regardless of LLAER waves considered. N1 and/or N2 results were most likely to reveal significant differences between children with CAPD and normal-hearing controls and could potentially be considered a biomarker for CAPD. CONCLUSIONS: It seems that LLAER assessments, especially waves N1 and N2, might assist in better identification of CAPD children. However, considering heterogeneity in the methodology among the included studies, the results should be interpreted with caution. Well-designed studies on children with confirmed CAPD using standard diagnostic and assessment protocols are suggested.
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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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".