Predictors of computed tomography imaging in patients presenting with sudden hearing loss
Bibliographic record
Abstract
Abstract Objective Sudden sensorineural hearing loss (SSNHL) is a rare presentation requiring timely diagnosis and treatment. Despite recommendations against obtaining computed tomography (CT) imaging of the head in clinical practice guidelines, this investigation is often completed in patients with sudden hearing loss. The aim of this study was to determine the proportion of patients undergoing CT imaging of the head for SSNHL at our center and identify predictive factors for the use of CT imaging. Methods Retrospective chart review of adult patients referred for SSNHL to two academic otology/neurotology practices between January 2018 and May 2021. Patient demographics, comorbid medical conditions, associated symptoms, location of initial presentation, audiologic results, and completed imaging studies were collected. Statistical analysis was performed with SPSS software. Results Ninety‐eight patients with audiologically confirmed SSNHL were included. Twenty‐two patients (22.4%) underwent CT imaging as an investigation for SSNHL. The presence of vertigo (odds ratio 6.90; 95% confidence interval 2.43, 19.56) and presentation to the emergency room (odds ratio 8.71; 95% confidence interval 3.02, 25.16) were significantly associated with undergoing CT imaging. These two variables were statistically significant independent predictors of CT imaging on multivariate regression analysis (p = .01, p = .001, respectively). Conclusion A significant proportion of patients with SSNHL undergo low‐yield CT imaging of the head, particularly patients presenting to the emergency room with vertigo. These results highlight an opportunity for focused education and quality improvement initiatives. Level of evidence: 4.
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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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".