The early auditory development of finnish children with cochlear implants evaluated with the LittlEARS® auditory questionnaire
Bibliographic record
Abstract
OBJECTIVES: To assess if the LittlEARS® Auditory Questionnaire (LEAQ) shows early auditory development of Finnish children with cochlear implants (CIs), the links between LEAQ and background variables, and if language skills at 12 and 24 months of hearing age can be predicted by results from LEAQ or other measures at 6, 12 and 18 months. METHODS: 15 children with CIs (unilateral or bilateral CIs or CI and hearing aid) were evaluated six times with LEAQ during two first years after cochlear implantation. A background information questionnaire and six other measures were administered. The data were statistically analysed and compared to a previous dataset of normally hearing (NH) children (N = 318). RESULTS: Children with CIs received higher scores in LEAQ during the first 12 months of hearing age compared to NH children during their first 12 months of chronological age. Compared to other measures, LEAQ showed progress in auditory development at younger hearing age. Children with bilateral hearing received higher scores in LEAQ on average than children with unilateral CI. A pattern of positive associations between total score of LEAQ at 6- and 12-month hearing age and speech comprehension skills at 12 and 24 months was found. CONCLUSIONS: LEAQ is a well-functioning measure in assessing and evaluating the early auditory development of children with CIs soon after implantation. The LEAQ results at 6- and 12-months hearing age may help identify children needing extra support to develop their auditory skills.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".