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Record W2054813833 · doi:10.1097/mao.0b013e318281e215

Spoken Language Benefits of Extending Cochlear Implant Candidacy Below 12 Months of Age

2013· article· en· W2054813833 on OpenAlexaboutno aff
Johanna G. Nicholas, Ann E. Geers

Bibliographic record

VenueOtology & Neurotology · 2013
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsnot available
FundersNational Institute on Deafness and Other Communication Disorders
KeywordsMedicineCochlear implantCandidacyCochlear implantationSpoken languageAudiologyActive listeningVocabularyLanguage developmentImplantSpeech perceptionLinguisticsDevelopmental psychologySurgeryPsychology

Abstract

fetched live from OpenAlex

OBJECTIVE: To test the hypothesis that cochlear implantation surgery before 12 months of age yields better spoken language results than surgery between 12 and 18 months of age. STUDY DESIGN: Language testing administered to children at 4.5 years of age (± 2 mo). SETTING: Schools, speech-language therapy offices, and cochlear implant (CI) centers in the United States and Canada. PARTICIPANTS: Sixty-nine children who received a cochlear implant between ages 6 and 18 months of age. All children were learning to communicate via listening and spoken language in English-speaking families. MAIN OUTCOME MEASURE: Standard scores on receptive vocabulary, expressive, and receptive language (includes grammar). RESULTS: Children with CI surgery at 6 to 11 months (n = 27) achieved higher scores on all measures as compared with those with surgery at 12 to 18 months (n = 42). Regression analysis revealed a linear relationship between age of implantation and language outcomes throughout the 6- to 18-month surgery-age range. CONCLUSION: For children in intervention programs emphasizing listening and spoken language, cochlear implantation before 12 months of age seems to provide a significant advantage for spoken language achievement observed at 4.5 years of age.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.616
Threshold uncertainty score0.582

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.022
GPT teacher head0.281
Teacher spread0.259 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations90
Published2013
Admission routes1
Has abstractyes

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