Exploring the relationship between head anatomy and cochlear implant stability in children
Bibliographic record
Abstract
In our experience, surgical outcomes in children have been excellent with a low complication rate. Our aim in this study was to better understand what aspects of our current surgical technique have been successful with a view to retain those that are beneficial as we proceed with implantation of future devices. Because the receiver-stimulator and overlying skin flap may be more vulnerable to damage in children than adults, we concentrated on issues related to the positioning and security of this part of the implant on the head. Three specific areas of vulnerability were explored in separate experiments. In Experiment 1, we determined the effect of the position of the device on the ability of a child to roll their head without allowing contact between the device and a supporting surface. The 'freeroll' angle was determined for devices position conventionally (back position) and for those in which the device is placed in a more anterior position (up position). In Experiment 2, we studied the retentive capacity of the child's pericranium and measured the displacement force required to dislodge an implant from the bed if retained by the calvarium only. In Experiment 3, we compared the skull curvature of children in whom the device was placed in the back versus the up position. These results inform us as how to best proceed with implantation in children using future devices that have thinner and wider receiver-stimulators.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".