Glasgow Benefit Inventory in Cochlear Implantation: A Reliable Though Ancillary Quality of Life Metric
Bibliographic record
Abstract
OBJECTIVE: The Glasgow Benefit Inventory (GBI) is a health-related quality of life instrument used to detect changes in health status following otolaryngologic interventions. Despite its use in cochlear implant literature, assessment of utility, reliability, and validity of GBI in an adult cochlear implants (CI) patient population has yet to be performed. STUDY DESIGN: Retrospective case series. SETTING: Academic, tertiary referral center. PATIENTS: Postlingually deafened, adult CI patients with at least 1 year of device use. INTERVENTIONS: Five hundred fifty-two patients were administered GBI questionnaires at least 1 year following CI activation during follow-up visits. MAIN OUTCOME MEASURES: GBI total and subscale scoring were compared to either the Hearing Handicap Inventory for Adults or Hearing Handicap Inventory for the Elderly. Moreover, a factor analysis and Cronbach's alpha were performed to determine GBI validity and internal reliability, respectively. RESULTS: The average overall GBI score was 38.6 ± 21.7. This was weakly correlated to the reduction in Hearing Handicap Inventory for Adults/Hearing Handicap Inventory for the Elderly (τb = 0.282, p < 0.05). High factor loading with minimal cross-loading was noted on a three-factor solution, which emulated the original GBI development. Internal reliability was acceptable for the general benefit (α = 0.913) and social support subclasses (α = 0.706), whereas physical health's was low (α = 0.643). CONCLUSIONS: Although GBI possesses adequate convergent and discriminant validity with acceptable reliability, its routine use to capture CI-specific health-related changes should not supersede validated CI-specific QoL instruments.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".