The Feasibility of Magnetic Resonance Imaging Without General Anesthesia Using the “Bundle and Scan” Technique for Infants With Sensorineural Hearing Loss
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study is to determine the feasibility of magnetic resonance imaging (MRI) without general anesthesia (GA) for infants being evaluated for sensorineural hearing loss (SNHL) using the bundle and scan technique. STUDY DESIGN: Retrospective study. SETTING: Pediatric tertiary care hospital. PATIENTS: All infants who underwent MRI using the bundle and scan technique as part of the diagnostic workup for unilateral or bilateral SNHL between June 2016 to April 2019 were included. MAIN OUTCOME MEASURE: The primary outcome was the proportion of clinically useful images produced. RESULTS: We reviewed 21 bundle and scan MRI examinations in infants being evaluated for SNHL. Patients had a median age of 10 (range: 6-25) weeks at the time of MRI. Motion artifact was noted in 38% (8/21) of cases. Eighty-six percent (18/21) of the magnetic resonance images produced using the bundle and scan technique were of diagnostic quality and/or sufficient for surgical planning for cochlear implantation. Repeat imaging with GA was required for three cases (14%) as the initial images were not clinically useful. All patients requiring GA had unilateral SNHL. All patients with bilateral SNHL successfully underwent MRI without GA using the bundle and scan technique. CONCLUSION: The results of our study demonstrate that it is feasible to perform MRI using the bundle and scan technique in the majority of young infants being evaluated for SNHL. This has the potential to help determine cochlear implant candidacy earlier, reduce exposure to GA, and reduce healthcare costs.
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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 | 0.002 | 0.014 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".