Commentary: Effect of cochlear implantation on vestibular function in children: A scoping review
Bibliographic record
Abstract
This recent scoping review on the effects of cochlear implantation on vestibular function in children, published in September 2022 by Gerdsen et al. (1), follows our group's own previous systematic review on the same topic, published in 2019 in the Journal of Otolaryngology -Head and Neck Surgery (2).Despite their comprehensive literature review, the authors did not cite our similar review which included many of the same references; a discussion on the differences in the evaluation, results, and conclusions between the two papers would have made an interesting addition, and we feel that further discussion of the analyses is a necessary addition to this area of research.Overall, we agree that the effect of cochlear implantation on objective and subjective vestibular findings in children is largely understudied and poorly understood.Accordingly, we wish to compare and contrast the findings of this updated review to the findings of our 2019 systematic review.Firstly, the inclusion criteria for both studies were similar, examining children under the age of 18 who received cochlear implantation and had pre-and post-operative vestibular testing performed.Gerdsen et al. (2022) included a total of 14 relevant studies compared to the 11 studies included in our previous analysis.The inclusion of four new studies, one case series and three cohort studies, represents an updated review of the literature during the intervening three
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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 | 0.013 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.014 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".