Post-operative patient perception of decisional regret in cochlear implant recipients
Bibliographic record
Abstract
IMPORTANCE: Decision regret post-surgery has has been linked to health outcomes for a number of elective procedures but is understudied in cochlear implantation satisfaction. Theunpredictability in outcomes may lead to unmet expectations by the recipient. This study is the first study to investigate the decision regret concept in cochlear implant recipients. OBJECTIVE: Tto investigate post-operative decision regret in (CI) recipients. DESIGN: This was a prospective cohort study using the validated Ottawa Decision Regret Scale, and whether the CI met the patient's expectations. Variables potentially associated with decision regret including patient demographics, post-operative speech perception scores, duration of deafness, duration of CI use and age were analyzed using the logistic regression model. SETTING: This was a multi-center study. Participants were recruited and enrolled from the University of Miami and the University of Kansas in an outpatient setting. PARTICIPANTS: Adult, English-speaking CI recipients with at least 6 months of listening experience with their implant. RESULTS: = 0.02) with poor post-operative speech perception. No other variables were associated with regret. CONCLUSIONS: Post-operative decision regret was reported by 42% of CI recipients. Poor speech perception abilities were associated with increased risk of regret. Further research is required to identify regret risks and provide resources to mitigate regret in CI recipients.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".