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Record W2981286853 · doi:10.1177/1053815119880944

Early Vocabulary in Children With Cochlear Implants: A Comparison Between Three Assessment Methods

2019· article· en· W2981286853 on OpenAlexafffund
Louise Duchesne, Natacha Trudeau, Andrea A. N. MacLeod, François Bergeron, Elin Thordardottir

Bibliographic record

VenueJournal of Early Intervention · 2019
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsMcGill UniversityUniversité LavalUniversité de MontréalUniversité du Québec à Trois-Rivières
FundersRéseau Provincial de Recherche en Adaptation-Réadaptation
KeywordsVocabularyPsychologyLanguage developmentChecklistVocabulary developmentDevelopmental psychologyAssistive technologyHearing lossAudiologyLinguisticsCognitive psychologyMedicineComputer science

Abstract

fetched live from OpenAlex

In children with a hearing loss who receive cochlear implants (CIs) under the age of 2, regular assessments are conducted to monitor auditory and linguistic progress. However, the collection of authentic, representative, and reliable expressive language data on young children with CIs remains a challenge. The purpose of the study was to determine whether data from parental report, language diary, and spontaneous language sample are equally representative of the development of expressive vocabulary over the first 12 months of CI use. Nine French-speaking children and their families participated in the study. We collected data at 3, 6, 9, and 12 months post-implantation, and we measured parental satisfaction regarding the use of a language diary. All three methods showed a progression in the total number of different words expressed over time and captured grammatical diversity. The findings suggest that when vocabulary size is still small, the diary might provide a more comprehensive picture of development than a vocabulary checklist.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.042
GPT teacher head0.393
Teacher spread0.351 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2019
Admission routes2
Has abstractyes

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Same venueJournal of Early InterventionSame topicHearing Loss and RehabilitationFrench-language works237,207