Cost Comparison of Auditory Brainstem Response versus Magnetic Resonance Imaging Screening of Acoustic Neuroma
Bibliographic record
Abstract
The cost-effectiveness of current diagnostic approaches employed in patients with suspected acoustic neuroma was evaluated. Currently, patients with signs and symptoms suggestive of acoustic neuroma, such as sudden unilateral hearing loss and/or tinnitus, undergo auditory brainstem response (ABR) screening tests to rule out this condition. If the ABR is normal, acoustic neuroma can be ruled out. However, if the ABR is abnormal, magnetic resonance imaging (MRI) or computed tomography is necessary to confirm the diagnosis. When one considers the total costs of this screening approach, one can ask whether straight MRI screening of all of these patients would be a more cost-effective approach to diagnosing this condition. A retrospective chart review of patient records obtained from the acoustic diagnostics laboratory at Hotel Dieu Hospital, Kingston, Ontario, was performed. A database of patients who have undergone ABR testing over the past 2 years was compiled and analyzed to assess how many of them went on to receive MRI. The total costs (based on Ontario Health Insurance Plan [OHIP] fee schedule rates) of this approach were compared with the estimated costs of straight MRI screening performed on the same patient population. By making such an analysis, decisions regarding the most cost-effective approach to screening for acoustic neuroma can be objectively assessed.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".