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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".