Cross-sectional survey of hearing impairment and ear disease in Uganda.
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence and causes of disabling hearing loss in adults and children in Uganda. STUDY DESIGN: Cross-sectional survey of ear disease and hearing impairment. SETTING: A random cluster sample design of the population from the Masindi district of Uganda following the World Health Organization (WHO) guidelines, using a modified version of the WHO Ear Disease Survey Protocol. MAIN OUTCOME MEASURE: The prevalence of disabling hearing impairment using the WHO definitions (excluding 0.5 kHz owing to high background noise levels). RESULTS: In the study, 6041 participants were enrolled and underwent audiometric evaluation and an ear examination. The prevalence of disabling hearing impairment was 11.7% in adults and 10.2% in children. A further 2.3% of children in whom thresholds could not be measured were deemed to have significant hearing loss based on screening questions and/or sound-field stimuli. Correctable causes such as dry perforations, cerumen impaction, and chronic suppurative otitis media resulted in disabling hearing loss in 17% of adult subjects and 41% of children. Preventable hearing loss, such as meningitis and noise-induced hearing loss, was present in a further significant percentage of subjects. CONCLUSIONS: Ear disease and hearing impairment were found to be important health problems in the Ugandan population. Preventable ear disease is a major cause of hearing loss in the population. It is hoped that the findings of this study will draw attention to the problem in Uganda and will lead to proper allocation of resources for the prevention and treatment of hearing loss and ear disease.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".