The relationship between auditory steady-state response and behavioural audiometry in hearing estimation for infants: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: Auditory Steady-State Response (ASSR) allows the identification of infants with hearing loss and consequently early intervention. Therefore, it is important to assess the accuracy of ASSR for determining hearing thresholds in infants. This study aimed to systematically review the threshold differences between air-conducted ASSR and behavioural audiometry (BA) in infants. METHODS: The population was infants younger than 2 years; the intervention was ASSR thresholds; the comparator was BA thresholds; and the outcome was the ASSR-BA threshold, i.e., the correction value in dB. PubMed, Web of Science, The Cochrane Central Register of Controlled Trials, and Embase were searched. The risk of bias was evaluated using the Evidence Project risk of bias tool and the Newcastle-Ottawa Quality Assessment Scale. The mean and 95% confidence intervals (CI) were calculated for the threshold differences at four frequencies (0.5, 1, 2, and 4 kHz). RESULTS: between 89 and 96%, p < 0.01). The mean differences (± 95% CI) between ASSR thresholds and BA hearing thresholds were 9.24 dB (± 6.45), 7.19 dB (± 4.02), 6.35 dB (± 4.51), and 7.42 dB (± 5.40) at 0.5, 1, 2, and 4 kHz, respectively. CONCLUSIONS: ASSR provides a reasonably accurate prediction of BA thresholds. Given the heterogeneity of the included studies, further research with larger infant populations is needed. SYSTEMATIC REVIEW REGISTRATION: The study was pre-registered in PROSPERO and followed PRISMA-P 2015 statement.
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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 | 0.017 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.041 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".