Evidence map›Paper›PMID 40426651›Full record

ReviewBrain sciences2025

Perfecting Sensory Restoration and the Unmet Need for Personalized Medicine in Cochlear Implant Users: A Narrative Review.

Archana Podury, Brooke Barry, Karen C Barrett, Nicole T Jiam

Abstract readReview
In one paragraph

Review in Brain sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Archana PoduryDepartment of Head and Neck Surgery, University of California, San Diego, CA 92093, USA.ORCID 0000-0001-5042-9196
Brooke BarryDepartment of Head and Neck Surgery, University of California, San Francisco, CA 94143, USA.
Karen C BarrettDepartment of Head and Neck Surgery, University of California, San Francisco, CA 94143, USA.ORCID 0000-0002-8991-4778
Nicole T JiamDepartment of Head and Neck Surgery, University of California, San Francisco, CA 94143, USA.ORCID 0000-0001-9838-5065

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hearing loss is one of the most common and undertreated medical conditions worldwide, with an estimated 466 million people (5% of the world's population) reporting disabling hearing impairment. The implications are significant; untreated hearing loss increases the risk of depression, social isolation, unemployment, cognitive decline, and falls. Cochlear implants (CIs) are surgically implanted electrical devices that allow people with severe hearing loss to process sound. Over the past 50 years, CI development has made remarkable ground, such that most CI users have adequate speech perception in a silent environment. These language achievements, while significant milestones, fall short of perfect sensory restoration. Many of these limitations with complex sound perception are due to our one-size-fits-all approach towards CIs and speech-based metrics for evaluating implant performance. In the past decade, there has been exponential interest in improving CI-mediated music perception, as it serves as a key conduit to restoring normal hearing. The present literature demonstrates the need for a personalized approach towards cochlear implantation and management. Our proposed narrative review illustrates the limitations of CI-mediated sound processing and discusses ways in which precision medicine can be introduced into the ever-expanding hearing loss population.

Indexed as

cochlear implanthearing lossmusicpitch perceptionprecision medicine

Identifiers

PMID40426651
PMCPMC12109860

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.