RAPID ASSESSMENT OF BILATERAL COCHLEAR IMPLANTATION FOR CHILDREN IN KAZAKHSTAN
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to evaluate the effectiveness of bilateral cochlear implantation (CI) compared with unilateral CI for deaf children in the context of the Republic of Kazakhstan health system. Methods. A literature search was conducted, using the PubMed, Cochrane, and Embase data bases for studies that compared the effectiveness of bilateral and unilateral CI in children. The search included English language, publications from 2002-2012. Two reviewers independently evaluated all relevant studies. Administrative data relevant to CI in Kazakhstan were obtained from the Ministry of Health. RESULTS: Three relevant systematic reviews and an health technology assessment report were found. There was evidence of incremental benefits from bilateral CI but the quality of the available studies was poor and there was little information on longer term outcomes. No conclusions could be drawn regarding later incremental improvements to speech perception, learning, and quality of life. To date, in the Republic of Kazakhstan there is not full coverage of audiological screening due to the lack of medical equipment. This leads to late detection of hearing-impaired children and a long rehabilitation period, requiring more resources. Age of implantation in children is late and only a small minority attend general schools. CONCLUSIONS: The clinical effectiveness of bilateral CI, an expensive health technology, requires further study. Given the current situation in Kazakhstan with audiological screening and access to unilateral CI, there appeared to be other priorities for improving services for children with profound hearing impairment.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".