Speech Perception Outcome in Multiply Disabled Children Following Cochlear Implantation: Investigating a Predictive Score
Bibliographic record
Abstract
BACKGROUND: Children with multiple disabilities account for a small percentage of implantees in a cochlear implant program, but they remain the most challenging group for which to predict benefit from the implant and for cooperation with habilitation postoperatively. PURPOSE: To assess the relationship of pre-implant functional disabilities with postoperative speech perception scores and determine the feasibility of predicting outcome with a cochlear implant in a multiply disabled pediatric population. RESEARCH DESIGN: Retrospective cohort study. STUDY SAMPLE: Sixty-six children with a cochlear implant and at least one additional disability. DATA COLLECTION AND ANALYSIS: We retrospectively examined the relationship between pre-implant Graded Profile Analysis (GPA) scores and postimplant speech perception scores. A pre-implant functional disability score (based on the Battelle developmental screen) was applied to the same cohort of patients and its association with postimplant speech perception scores was examined. RESULTS: The functional disability score significantly predicted high (k > 24) and low (k < 7) speech perception scores (p < 0.001 and p < .0001) and had excellent discrimination ability (c statistic = 0.88 and 0.93 respectively). The GPA score was not significantly associated with speech perception scores (p = 0.519 and p = 0.146) and demonstrated no ability to discriminate postimplant speech perception scores in this implant population (c statistic = 0.49 and c = 0.57). CONCLUSIONS: Prediction of outcomes following cochlear implantation in multiply disabled children can be facilitated using this newly developed functional disability score as an adjunct to traditional candidacy assessments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".