Cochlear Implantation in Patients with Age-Related Hearing Loss: A Bibliometric Analysis
Bibliographic record
Abstract
Background: The prevalence of age-related hearing loss has become increasingly prominent, with a year-on-year rise in the number of elderly patients with presbycusis undergoing cochlear implantation. Nevertheless, there remains a scarcity of systematic, chronologically oriented comprehensive research on this topic. The present study employs bibliometric analysis to identify research trends and current hotspots pertinent to this theme.Methods: Relevant studies on cochlear implantation for presbycusis indexed in the Web of Science Core Collection database were retrieved, covering the period from January 1, 2005, to December 31, 2024. Employing bibliometric tools including Vosviewer, CiteSpace, and Bibliometrix R, systematic bibliometric statistical and visual analyses were conducted on the included research literature.Results: A total of 2,331 documents were included in this analysis, reflecting a growing volume of scientific research on this topic alongside increasingly significant scholarly contributions. The United States maintains a leading position with 759 publications, while the University of Toronto System tops institutional rankings with 173 publications. At the author level, CARLSON MATTHEW L. leads with 40 publications, and MOBERLY AARON C. exhibits notable growth potential. In terms of research evolution, the field has progressively shifted from early investigations into etiological mechanisms toward a focus on clinical phenotypes, intervention strategies, and prognostic evaluation.Conclusions: As publications focusing on cochlear implantation research for presbycusis continue to proliferate, bibliometric analysis serves as a valuable tool to help researchers delineate international academic collaborations and discern trending themes within this specialized research domain.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.129 | 0.155 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".