Community-based newborn hearing screening programme for early detection of permanent hearing loss in Iran: An eight-year cross-sectional study from 2005 to 2012
Bibliographic record
Abstract
OBJECTIVES: We here present the results of the first eight years of the newborn hearing screening programme in Iran, with a view to establishing the prevalence of hearing impairment among infants, and the efficacy of the programme. METHODS: A total of 3,350,995 infants were screened using the series method of transient evoked otoacoustic emissions (TEOAEs)/automated auditory brainstem responses (AABRs), between 2005 and 2012. The infants were first tested for TEOAEs (three times). Based on the results of this test, the positive cases were referred to the next stage, where they were tested for AABRs. If they also tested positive on AABRs, they were referred to the diagnostic and rehabilitation stages. RESULTS: Results of this study indicated an infant hearing impairment prevalence of 3 per 1000. Although this rate was as high as 5 per 1000 in the early years of the programme, it decreased to 2.6 per 1000 in the last year. The absolute referral rate was 14.5% in the first stage, which decreased to 0.9% and 0.2% in the second and the third stages, respectively. The follow-up rate was 70% in the first stage, which increased up to 73% and 85% in the second and the third stages, respectively. CONCLUSION: The study results suggest that the prevalence of hearing impairment in infants in Iran is comparable with that in developed and developing countries, and that the series TEOAEs/AABRs method used in the screening programme in Iran is efficient.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".