Comorbidity associated with worse outcomes in a population of limited cochlear implant performers
Bibliographic record
Abstract
Abstract Introduction Most patients significantly benefit from cochlear implantation (CI). However, speech understanding varies widely, with a small proportion of patients demonstrating limited audiometric outcomes. While there are well‐documented determinants of poor performance, there remains a cohort of patients that do not meet expected outcomes. Preoperative prognostication is desirable to manage expectations, ensure value of the intervention, and reduce risk. The objective of the study is to evaluate variables found within a single CI center's most limited functioning cohort following implantation. Methods A retrospective review of a single CI program's cohort of (344 ears) patients implanted between 2011 and 2018 whose 1‐year postimplantation AzBio scores fall 2 SDs below the mean was performed. Exclusion criteria includes skullbase pathology, pre/peri‐lingual deafness, cochlear anatomic abnormalities, English as an additional language, and limited electrode insertion depth. Overall, 26 patients were identified. Results The study population's postimplantation net benefit AzBio score is 18% compared to the entire program's 47% ( p < 0.05). This group is older (71.8 vs. 59.0 years, p < 0.05) with a longer duration of hearing loss (26.4 vs. 18.0 years, p < 0.05) and with a lower preoperative AzBio score [14% lower ( p < 0.05)]. A host of medical conditions were identified in the subpopulation, with a trend towards significance in those suffering from either malignancy or cardiac condition. Escalating comorbid status was associated with worse performance ( p < 0.05). Conclusion Within a cohort of limited‐performing CI users, benefit tended to decrease with escalating number of comorbid conditions. This information may serve to inform preoperative patient counseling. Level of evidence Level IV (evidence from a case control study).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".