Power spectrum auditory brainstem response: novel approach to the evaluation of patients with unilateral auditory symptoms.
Bibliographic record
Abstract
BACKGROUND: Unilateral hearing loss and audiovestibular symptoms are common in the population and can be associated with retrocochlear tumours such as acoustic neuroma (AN). In order to rule out AN, many patients with asymmetric hearing loss are referred for magnetic resonance imaging (MRI). Recent work with modifications of auditory brainstem response (ABR) protocols has improved the ability of ABR to detect small acoustic tumours. This study presents our initial results using the power spectrum ABR (PSABR) as another tool in detecting patients at higher risk for AN. METHODS: A prospective, observational cohort design was employed and a total of 53 subjects were recruited (19 subjects were normal controls and 34 subjects were patients with unilateral audiovestibular symptoms). All subjects underwent complete auditory testing, standard ABR, stacked ABR, and power spectrum ABR. The 34 patients also underwent gadolinium enhanced MRI. RESULTS: Using logistic regression, our data showed that wave I-V latency was most highly predictive of tumour presence or absence. However, both stacked ABR and power spectrum ABR were predictive. Stacked ABR was also able to differentiate symptomatic patients with tumours from those without. CONCLUSIONS: This study was not designed to compare PSABR to other more established methods. Rather, our intent was to establish PSABR as a valid and practical addition to other ABR tools. More research is needed to optimize PSABR algorithms, and the method also needs to be tested and validated in larger populations of patients. Early results do indicate that PSABR could be a valid and reliable method of identifying subgroups of patients with unilateral auditory dysfunction who would best benefit from MRI.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".