The management of acute mastoiditis in children with cochlear implants: Saving the device
Bibliographic record
Abstract
OBJECTIVE: Early treatment of profound bilateral sensorineural hearing loss with cochlear implantation has become routine, resulting in an increased proportion of children implanted at younger ages. These children are at a relatively high risk for acute otitis media (AOM), and are more likely to develop mastoiditis in the implanted ear. Despite the significant risks associated with mastoiditis, including compromise of the implant, there are no specific guidelines on the management of this population. We propose a treatment algorithm emphasizing early but conservative operative intervention. METHODS: A retrospective chart review included eight patients, who experienced mastoiditis, in the context of cochlear implantation at our center from August 2005 to November 2012. During this period 806 implant surgeries were completed. RESULTS: The median age at which mastoiditis occurred was 37 months, and the mean time from implantation to mastoiditis was 9.56 months. All patients underwent drainage of the middle ear in conjunction with intravenous antibiotics, and two additionally underwent post-auricular incision and drainage. DISCUSSION: Recent mastoidectomy may be a risk factor for the development of a post-auricular abscess in children, who develop AOM following cochlear implantation. A treatment algorithm was developed, which emphasizes early operative drainage in conjunction with aggressive antibiotic therapy. Conclusions A consistent approach to the management of mastoiditis in children with cochlear implants has not been established. Rapid initiation of aggressive antibiotic therapy and a low threshold for conservative operative intervention results in effective resolution of infection while allowing preservation of the implant.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".