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Record W3184018683 · doi:10.1177/23312165211031751

Cochlear Implantation in Infants: Why and How

2021· review· en· W3184018683 on OpenAlexaff
Patricia L. Purcell, Nicholas L. Deep, Susan B. Waltzman, J. Thomas Roland, Sharon L. Cushing, Blake C. Papsin, Karen A. Gordon

Bibliographic record

VenueTrends in Hearing · 2021
Typereview
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsCandidacyMedicineCochlear implantationCochlear implantPerioperativeAudiologyIntervention (counseling)PediatricsSurgeryPsychiatry

Abstract

fetched live from OpenAlex

In children with congenital deafness, cochlear implantation (CI) prior to 12 months of age offers the opportunity to foster more typical auditory development during late infancy and early childhood. Recent studies have found a positive association between early implantation and expressive and receptive language outcomes, with some children able to achieve normal language skills by the time of school entry. Universal newborn hearing screening improved early detection and diagnosis of congenital hearing loss, allowing for earlier intervention, including decision-making regarding cochlear implant (CI) candidacy. It can be more challenging to confirm CI candidacy in infants; therefore, a multidisciplinary approach, including objective audiometric testing, is recommended to not only confirm the diagnosis but also to counsel families regarding expectations and long-term management. Surgeons performing CI surgery in young children should consider both the anesthetic risks of surgery in infancy and the ways in which mastoid anatomy may differ between infants and older children or adults. Multiple studies have found CI surgery in infants can be performed safely and effectively. This article reviews current evidence regarding indications for implantation in children younger than 12 months of age and discusses perioperative considerations and surgical technique.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score0.731

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.146
GPT teacher head0.398
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations46
Published2021
Admission routes1
Has abstractyes

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