Measuring the Advantage of Kalman-Weighted Averaging for Auditory Brainstem Response Hearing Evaluation in Infants
Bibliographic record
Abstract
PURPOSE: The purposes of this study were to (a) measure the effects of Kalman-weighted averaging methods on auditory brainstem response (ABR) threshold, latency, and amplitude; (b) translate lab findings to the clinical setting; and (c) estimate cost savings when ABRs can be obtained in nonsedated infants. METHOD: ABRs were recorded in 40 adults with normal hearing during induced motor noise conditions using the Kalman-weighted averaging method implemented on a commercial system, the Vivosonic Integrity (Vivosonic Inc., Toronto, Ontario, Canada). The device was then used to test 34 infants in awake and asleep states. The advantages of the Kalman-weighted averaging method were modeled in terms of time saved for conducting an ABR evaluation. RESULTS: Kalman-weighted ABR threshold estimates were 6-7 dB lower than with conventional methods during induced motor noise. When used to obtain ABRs in infants who were awake, the number of sweeps required to obtain a result was significantly greater than that required for a sleeping infant but well within the range for clinical application. CONCLUSIONS: The use of Kalman-weighted averaging provides a measurable advantage over conventional methods and may reduce costs for the pediatric audiology practice.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".