Development of an Effective Public Health Screening Program to Assess Hearing Disabilities among Newborns in Shanghai: A Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: An effective, systematic program of screening, diagnosis and intervention against hearing loss in infants could help them avoid developmental impediments and could help society stem preventable healthcare burdens. We assessed the feasibility and outcomes of a new public health universal newborn hearing screening program (UNHSP) for neonates born in Shanghai. METHODS: From March 2002 to June 2007, we conducted a two-stage hearing loss screening program for neonates born at all 105 delivery hospitals in Shanghai. Institutional participants in the program followed standardized testing criteria and procedures. The first stage of screening occurred in the hospital during the third day post-birth; positive infants underwent a second-stage outpatient screening on Day 42. Positive infants were examined at clinical diagnosis centres, and interventions were conducted at rehabilitation centres. In 2003, a random sample of parents were interviewed about the program, and their level of stress over learning their child screened positive were recorded and analyzed. FINDINGS: Overall, our program screened 72.98% of eligible infants and provided effective interventions within six months to 86.31% of those with hearing oss. During the 5-year study, first-stage screenings assessed 90.85% of 616,880 eligible infants and found 12.16% positive. Day 42 screenings had a 65.68% participation rate and a positive rate of 14.75%. Of these, 0.146% were deemed permanently hearing impaired within three months of birth. Parental satisfaction measures exceeded 90%. INTERPRETATION: A government-sponsored public health program to screen, diagnose, treat and provide interventions for all newborns with permanently hearing impairment can be effectively implemented and can achieve outcomes that surpass comparable clinical initiatives.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".