Early Detection of Pediatric Permanent Hearing Loss: A Population-Based Retrospective Cohort Study
Bibliographic record
Abstract
Evidence has indicated that Early Hearing Detection and Intervention (EHDI) programs have lowered the age of identifying permanent hearing loss (PHL) in childhood, a first step to intervention. However, limited research has compared the age of diagnosis before and after implementation in one jurisdiction across the full range of PHL. Methods: Children diagnosed with PHL in Ottawa, Canada were identified from the Child Hearing Lab database at the Children’s Hospital of Eastern Ontario and linked to Ontario health administrative data. Age of identification of PHL of children born in the pre-infant hearing program (IHP) era (1991-2002) was compared to those born in the decade after it (2003-2013), employing a regression discontinuity design. Results: Age at identification of PHL declined more rapidly in the post-IHP era compared to the pre-IHP period (β estimate of IHP*Time -2.12, 95% CI -0.59 to -3.65, P=0.007). This association remained when interactions between IHP, time, and severity of hearing loss were included (β IHP*Time -6.05, 95% CI -2.59 to -9.51, P<0.001). Discussion: Implementation of Ontario’s EDHI program was associated with earlier diagnosis of PHL compared to the era before its implementation. This finding provides direct evidence of effectiveness across the range of PHL in one jurisdiction.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".