Cochlear implantation: a personal and societal economic perspective examining the effects of cochlear implantation on personal income.
Bibliographic record
Abstract
OBJECTIVE: Although cochlear implantation has been shown to improve quality of life, the socioeconomic benefit to the individual and society has not been thoroughly investigated. Our objective was to determine the economic impact of profound deafness and subsequent effects of unilateral cochlear implantation. DESIGN: Retrospective analysis of a prospectively collected cochlear implantation database. SETTING: An academic, tertiary care hospital. METHODS: A prospectively collected cochlear implantation database of 702 patients was reviewed. Known Canadian economic surrogates were used to estimate the personal economic impact of both deafness and unilateral cochlear implantation. MAIN OUTCOME MEASURES: The main outcome measures included employment rates and personal income prior to and following cochlear implantation. RESULTS: A total of 637 patients had sufficient occupational data for inclusion in the study; 36.7% suffered a negative economic impact as a result of their deafness. Cochlear implantation was associated with a significant increase in median yearly income compared to preimplantation ($42 672 vs $30 432; p = .007). CONCLUSIONS: Cochlear implantation not only improves quality of life but also translates into significant economic benefits for patients and the Canadian economy. These benefits appear to exceed the overall costs of cochlear implantation.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".