Diagnostic Yield of MRI for Sensorineural Hearing Loss – An Audit
Bibliographic record
Abstract
PURPOSE: Contrast-enhanced magnetic resonance imaging (CEMRI) of the head is frequently employed in investigations of sensorineural hearing loss (SNHL). The yield of these studies is perceptibly low and seemingly at odds with the aims of wise resource allocation and risk reduction within the Canadian healthcare system. The purpose of our study was to audit the use and diagnostic yield of CEMRI for the clinical indication of SNHL in our institution and to identify characteristics that may be leveraged to improve yield and optimize resource utilization. MATERIALS AND METHODS: The charts of 500 consecutive patients who underwent CEMRI of internal auditory canal for SNHL were categorized as cases with relevant positive findings on CEMRI and those without relevant findings. Demographics, presenting symptoms, interventions and responses, ordering physicians, and investigations performed prior to CEMRI testing were recorded. Chi-squared test and t-test were used to compare proportions and means, respectively. RESULTS: CEMRI studies revealed relevant findings in 20 (6.2%) of 324 subjects meeting the inclusion criteria. Pre-CEMRI testing beyond audiometry was conducted in 35% of those with relevant positive findings compared to 7.3% of those without (p < 0.001). Auditory brainstem response/vestibular-evoked myogenic potentials were abnormal in 35% of those with relevant CEMRI findings compared to 6.3% of those without (p < 0.001). CONCLUSION: CEMRI is a valuable tool for assessing potential causes of SNHL, but small diagnostic yield at present needs justification for contrast injection for this indication. Our findings suggest preferred referral from otolaryngologists exclusively, and implementation of a non-contrast MRI for SNHL may be a better diagnostic tool.
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 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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".