Computed Tomography and/or Magnetic Resonance Imaging Before Pediatric Cochlear Implantation? Developing an Investigative Strategy
Bibliographic record
Abstract
OBJECTIVE: To investigate and compare the usefulness of preoperative magnetic resonance (MR) imaging and high-resolution temporal bone computed tomography (HRCT) in pediatric cochlear implant candidates. STUDY DESIGN: Prospective. SETTING: Tertiary referral center. PATIENTS: A cohort of 92 pediatric patients with profound hearing. Inclusion criteria were MR, computed tomography, and cochlear implantation. INTERVENTION. DIAGNOSTIC: All patients had preoperative imaging of the petrous temporal bone (HRCT, T2-weighted fast spin echo, axial 3D Fast Imaging Employing Steady-state Acquisition [FIESTA] MR) and brain (Fast Fluid-attenuated Inversion-recovery [FLAIR] MR). MAIN OUTCOME MEASURE(S): Overall prevalence of inner ear dysplasias in this population and comparison of detection rates between HRCT, T2 Fast Spin Echo (FSE), and FIESTA MR sequences. RESULTS: Radiological abnormalities were observed in 32 and 59% of MR and HRCT temporal bone, respectively. Synchronous intracranial findings were noted in 40% on brain MR. Common vestibulocochlear nerve was observed in 3% ears and directed side of implantation. Consistent discrepancies noted on HRCT were inability to diagnose early obliterative labyrinthitis and presence of the cochlear nerve in the internal auditory canal. With respect to MR, enlarged vestibular aqueducts and narrow cochlear nerve canals were consistently under identified. CONCLUSION: Dual-modality imaging with HRCT and MR of petrous bone and MR brain in the precochlear implant pediatric population detects abnormalities related to deafness, which would not otherwise be found using either modality alone. There is overlap between the imaging modalities in the type of abnormalities detected, and we present a case for selective use of HRCT within a diagnostic algorithm, using the patient risk factors we have identified.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".