The challenges of starting a cochlear implant programme in a developing country
Bibliographic record
Abstract
PURPOSE OF REVIEW: Deafness is indeed a silent disability in many parts of the world, and the majority of people who have hearing impairment live in developing countries. With rising economy and developing nations becoming hub of industrialization, hearing loss may increase in these countries. In this review, the authors have elected to focus the discussion on India to frame the challenges of cochlear implants in a developing country. This article reviews the common causes of hearing loss, the challenges faced by those with hearing impairment and why the penetration of these devices is low and also reviews some reasons for the inability of the government to support the implant programme in India. RECENT FINDINGS: Early identification of hearing is crucial towards ensuring appropriate hearing rehabilitation; it is, however, challenged by various factors, including public awareness, absence of a national new born screening programme, accessibility to diagnostic centres, availability of trained personnel and equipment and patient affordability. Cochlear implants are a proven auditory rehabilitative option for individuals with severe to profound sensorineural hearing loss, who otherwise do not benefiting from hearing aids. Nevertheless, only a small percentage of these individuals receive cochlear implants, and cost remains a leading prohibitive factor, particularly in developing countries. For example, in India, the personal average annual income is well below US $2000, whereas these devices cost between $12,000 and $25,000, exclusive of hospital and staff fees. Hence, the technology is virtually unavailable to the masses. SUMMARY: To overcome the cost limitation of those who would benefit from cochlear implants countries such as India and China have started to develop their own indigenous implants.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".