Detection of otoacoustic emissions in chinchilla when the middle ear contains amniotic fluid
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Otoacoustic emissions have frequently been used for newborn hearing screening. However, they have low specificities and high referral rates. The presence of amniotic fluid in the middle ear is one reason for these problems. The aim of this study was to determine the effects of human amniotic fluid on otoacoustic emissions and on middle-ear function. METHODS: Forty-six chinchillas were randomly divided into eight groups based on the type (amniotic fluid or normal saline) and volume (0.5, 1, 1.5, 2 ml) of liquid introduced into the middle ear. Distortion product otoacoustic emission (DPOAE) and wideband reflectance (WBR) measurements were taken under inhalational anaesthesia before and after introduction of middle-ear liquid. The differences in these measurements were subjected to statistical analyses. STUDY DESIGN: Prospective controlled animal study. RESULTS: Significant reductions of DPOAE levels and increases in WBR occurred across all frequencies when there was liquid in the middle ear, and the changes became greater for increased volumes of liquid. Changes in the noise level had important effects on the otoacoustic-emission signal-to-noise ratio at the three lowest frequencies. CONCLUSION: Both human amniotic fluid and saline in the chinchilla middle ear resulted in changes in otoacoustic-emission detection patterns and WBR that may be relevant to newborn hearing screening.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".