Assessment of Central Auditory Processing in Children Using a Novel Tablet-Based Platform: Application for Low- and Middle-Income Countries
Bibliographic record
Abstract
OBJECTIVE: Evaluate whether a portable, tablet-based central auditory processing (CAP) test system using native language training videos and administered by minimally trained community health workers can produce CAP results comparable to previously published norms. Our secondary aim was to determine subject parameters that influence test results. STUDY DESIGN: Cross-sectional study. SETTING: Community-based settings in Chontales, Nicaragua, New Hampshire, and Florida. PATIENTS: English- and/or Spanish-speaking children and adolescents (n = 245; average age, 12.20 yr; range, 6-18 yr). MAIN OUTCOME MEASURES: Completion of the following tests with responses comparable to published norms: Pure-tone average (PTA), gap detection threshold (GDT), fixed-level frequency threshold, masking level difference (MLD), Hearing in Noise Test (HINT), Dichotic Digits Test (DDT), and Frequency Pattern Recognition (FPR) test. RESULTS: GDT, HINT, and DDT had comparable results to previously published normative values. MLD and FPR results differed compared with previously published normative values. Most CAP tests (MLD, GDT, HINT) results were independent of age and PTA (p = 0.1-0.9). However, DDT was associated with age and PTA (p < 0.0001). CONCLUSIONS: Pediatric CAP testing can be successfully completed in remote low- and middle- income country environments using a tablet-based platform without the presence of an audiologist. Performance on DDT improved with age but deteriorated with hearing loss. Further investigation is warranted to assess the variability of FPR.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".