Is there a relationship between perceptual difficulties and auditory functioning at the periphery and the brainstem in children suspected with auditory processing disorder?
Bibliographic record
Abstract
The test battery typically used for the diagnosis of auditory processing disorder (APD) is highly heterogeneous, with an emphasis on the central auditory nervous system. As such, the peripheral auditory system is typically only screened for the presence of an overt hearing loss. Our previous work suggested that children suspected of APD (sAPD) have atypically sharp cochlear tuning when measured using stimulus frequency otoacoustic emission (SFOAE) group delay. In the present work, we extend our previous findings and test the hypothesis that cochlear tuning influences auditory brainstem response (ABR) latencies. Our hypothesis is based on filter theory, which suggests that a sharper filter will take longer to build-up and ring longer. We predicted that sharper cochlear filters in sAPD should result in delayed ABR wave latencies and will be associated with poorer performance on speech perception tests. Preliminary data from 16 sAPD and 6 typically developing children show a positive correlation between cochlear tuning and ABR peak I latency. Cochlear tuning explains a significant proportion of the variance in ABR peak I latency (R2 = 0.25). Further behavioral results and implications for inclusion of auditory peripheral examination in APD tests will be discussed.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".