Determining cochlear implant candidacy in children with residual hearing: A scoping review
Bibliographic record
Abstract
OBJECTIVES: The purpose of this review was to summarize what is known about criteria, assessments, and recommendations for evaluating cochlear implant (CI) candidacy in children with residual hearing. METHODS: Peer-reviewed studies were identified through a systematic search in five electronic databases. Articles were screened and assessed for eligibility. From the eligible studies, data were extracted to summarize and present a narrative synthesis of the findings. RESULTS: A total of seven articles (two reviews and five primary studies) were included in the final analysis. Hearing levels better than the moderately severe to severe range (65-90 dB HL) tend to be supported as audiological candidacy criteria for pediatric CI. Recommendations for candidacy consideration based on audiologic thresholds range from 65 to 80 dB Hl pure-tone average as the lower boundary. Our review did not identify any specific assessment protocols. However, additional decision-making considerations related to borderline hearing loss configurations and assessment tools (the Speech Intelligibility Index and the Pediatric Minimum Speech Test Battery) were identified. Supplementary assessment considerations were also reported. CONCLUSION: There is limited information regarding specific assessment protocols for children with residual hearing. The literature is primarily focused on guidelines related to audiologic criteria, although it is widely recommended that other areas of functioning should also be considered. Most recommendations appear to be based on expert opinion, clinical expertise, and evidence from overall pediatric CI outcomes rather than empirical evidence targeting children with residual hearing. There is an ongoing need for research to further develop protocols and tools that can assist clinicians and families in making cochlear implantation decisions for children with residual hearing.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".