Ten‐year health‐related quality of life in cochlear implant recipients
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: To evaluate the long-term impact of cochlear implantation on quality of life measured by the Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36). Scores were also converted to the SF-6D to derive health utility scores. STUDY DESIGN: Prospective cohort study. METHODS: Thirty-two patients undergoing cochlear implantation completed the SF-36 preoperatively, 1 year, and 10 years after cochlear implantation. RESULTS: SF-36 results showed improvements in seven of the eight attributes when preoperative scores where compared with 1- and 10-year results. Between 1 and 10 years postoperatively, six of eight domains deteriorated in scores. When converted to the SF-6D, the mean preoperative utility scores were 0.592 for standard gamble, 0.636 using the ordinal health state paradigm, and 0.579 using the Bayesian technique. Ten years postoperatively, health utility scores were 0.643 (standard gamble), 0.684 (ordinal health state), and 0.6 (Bayesian). Between preoperatively and 10-year postoperatively, improvements were therefore 0.051, 0.048, and 0.021 for standard gamble, ordinal health state, and Bayesian paradigm, respectively. CONCLUSIONS: This study establishes the long-term sustained benefits of cochlear implantation on quality of life. Nevertheless, both the SF-36 and SF-6D seem to underestimate the benefit accrued through this intervention. Our data are consistent with others regarding the unsuitability of the SF-36 in benefit assessment, notwithstanding that conversion to the SF-6D is feasible, and the SF-6D seemed to better depict possible benefits from cochlear implantation as compared to the SF-36.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".