Screening strategies for neonatal hearing loss: which test is best?
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to evaluate the accuracy and cost effectiveness of three different methods of hearing screening in newborns. DESIGN: A prospective, randomized cohort design was used. One hundred and five newborns were tested in this preliminary study. SETTING: The study was conducted in a tertiary care hospital setting in both the well baby and special care nurseries. METHODS: Consenting subjects had their hearing tested using automated auditory brainstem response (AABR), distortion-product otoacoustic emissions, and click-evoked otoacoustic emissions. The time to perform the tests was recorded and the cost of each test was calculated. MAIN OUTCOME MEASURES: The main outcomes measured were the time taken to perform each test, the pass/fail rate for each test, and the estimated cost of the tests. RESULTS: In this small cohort of patients, we found that AABR was the most accurate test, but it took longer to perform and was more expensive than either of the otoacoustic emission tests. However, the sensitivity and specificity of otoacoustic emissions were less than that of AABR. Test time decreased as the examiner gained experience, and we anticipate that experience will also result in better accuracy for the otoacoustic emission tests. CONCLUSIONS: Hearing screening in a hospital-based newborn population is both feasible and cost effective. Although AABR was more expensive, its better accuracy must be considered. As technology improves, the cost of all three tests will diminish. More robust conclusions cannot be made based on this small patient population.
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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 | 0.007 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".