Gene Therapy for Congenital Sensorineural Hearing Loss: A Future Perspective in Iraq
Bibliographic record
Abstract
Disabled congenital sensorineural hearing loss (SNHL) has major drawbacks to the affected individuals such as delay in speech, cognition, language development, and learning. As a consequence, the affected persons have difficulty in finding appropriate jobs and performing and maintaining their jobs. Additionally, hearing impairment is considered a stigma. Furthermore, they live in isolation, depression, and poor quality of life. Hearing loss is a common sensory deficit with an approximate incidence of 1 to 3 of every 1000 newborns across the globe with up to 60% of cases being due to genetic factors [1]. A previous systematic review (2020) of the genetic epidemiology of hearing impairment in 22 Arab countries found that the incidence of hereditary hearing loss ranged from 1.2-18 per 1000 birth per year and Iraq carries the highest prevalence (76.3%) [2]. Early detection is an essential aspect in the management of hearing loss in general and congenital one in particular. This step depends on the awareness of the parents about their babies’ hearing status. However, it doesn’t identify all cases because it depends on the parent's level of education. Therefore, a universal hearing screening program through otoacoustic emissions and auditory brainstem response tests is necessary to diagnose hearing loss in newborns. This program has two major drawbacks; First, it cannot diagnose mild or delayed onset hearing impairment in neonates. Second, it is unable to find the reason for the hearing loss. Nowadays, genetic testing, a universal hearing screening program, and tests for cytomegalovirus are recommended tools for the diagnosis of hearing loss in newborns. [3-5].
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".