Correction Factor Evaluation and Between-System Comparison of Behavioral Threshold Predictions From Auditory Brainstem Response Measures in Infants
Bibliographic record
Abstract
PURPOSE: Auditory brainstem response (ABR) thresholds are corrected to estimate behavioral thresholds in infants. Corrections were validated, and a comparison of behavioral threshold estimates between systems was conducted to inform equipment transition and protocols in Ontario, Canada. METHOD: In Study 1, a retrospective file review was conducted. ABR threshold estimates from 84 infants with hearing loss were compared to behavioral thresholds to validate the accuracy of the ABR corrections applied in the Ontario Infant Hearing Program since 2016. Study 2 examined the precision of two different ABR systems to estimate thresholds in 37 adult and 105 infant ears. RESULTS: Corrected ABR thresholds predicted behavioral thresholds in infants to within 1.77 dB (range of mean values across frequency: 1.18-2.26 dB) on average. The average differences decreased across frequency to 0.6 dB (range: 0.14 to -1.23) when ear canal acoustics were accounted for. The average between-system difference in ABR threshold estimates was 2.40 dB (range: 1.18-2.26). CONCLUSIONS: ABR correction factors used in Ontario's Infant Hearing Program provide accurate predictions of behavioral thresholds in infants with hearing loss. When calibration and collection parameters are similar between different ABR systems, threshold estimates are comparable and no further adjustment to correction factors was required.
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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 | 0.012 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".