Factors Affecting Auditory Performance of Postlinguistically Deaf Adults Using Cochlear Implants: An Update with 2251 Patients
Bibliographic record
Abstract
OBJECTIVE: To update a 15-year-old study of 800 postlinguistically deaf adult patients showing how duration of severe to profound hearing loss, age at cochlear implantation (CI), age at onset of severe to profound hearing loss, etiology and CI experience affected CI outcome. STUDY DESIGN: Retrospective multicenter study. METHODS: Data from 2251 adult patients implanted since 2003 in 15 international centers were collected and speech scores in quiet were converted to percentile ranks to remove differences between centers. RESULTS: The negative effect of long duration of severe to profound hearing loss was less important in the new data than in 1996; the effects of age at CI and age at onset of severe to profound hearing loss were delayed until older ages; etiology had a smaller effect, and the effect of CI experience was greater with a steeper learning curve. Patients with longer durations of severe to profound hearing loss were less likely to improve with CI experience than patients with shorter duration of severe to profound hearing loss. CONCLUSIONS: The factors that were relevant in 1996 were still relevant in 2011, although their relative importance had changed. Relaxed patient selection criteria, improved clinical management of hearing loss, modifications of surgical practice, and improved devices may explain the differences.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".