Patient Characteristics Associated With Speech Recognition and Quality of Life Improvement After Cochlear Implantation
Bibliographic record
Abstract
OBJECTIVE: To identify patient characteristics associated with improvements in speech recognition scores (SRS) and Cochlear Implant Quality of Life (CIQOL-35) scores following cochlear implantation. STUDY DESIGN: Multi-institutional prospective cohort. SETTING: Tertiary medical centers. PATIENTS: Two hundred thirty-five adult CI users with bilateral hearing loss. MAIN OUTCOME MEASURES: Pre-CI and 12-month post-CI CIQOL-35, CNC word (CNC), and AzBio quiet (AzBio) scores were obtained. On the basis of improvement beyond established minimally detectable change values for each CIQOL domain and 95% CIs of pre-CI SRS as compared with post-CI, the cohort was divided into 4 groups for each CIQOL domain/SRS pair: (A) CIQOL and SRS improvement, (B) CIQOL improvement only, (C) SRS improvement only, and (D) no CIQOL or SRS improvement. RESULTS: Correlations between CIQOL and SRS improvements were weak ( r =0.02-0.17). Grouped by CIQOL-Global/AzBio after 12 months post-CI, 51% of patients were classified as Group A, 4% as Group B, 39% as Group C, and 7% as Group D; percentages were similar for CIQOL-Global/CNC outcomes. Patients without CIQOL improvement had higher pre-CI CIQOL scores than patients who improved ( d =0.58-2.19), and patients without SRS improvement had higher pre-CI SRS scores than patients who improved (CNC: d =1.35-2.89, AzBio: d =1.09-2.78). Patients in Group D for the Entertainment domain were older than patients in Group A. Other patient factors were not significantly associated with the odds of improvement. CONCLUSIONS: Patients with higher baseline scores for a given outcome measure are less likely to improve following cochlear implantation. SRS and CIQOL score improvements were weakly correlated. The vast majority of patients (93%) improved in one or both outcome measures.
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 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 | 0.004 |
| 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 | 0.000 |
| 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".