Ringing in the Ears: Approaches to Imaging and Management of Tinnitus
Bibliographic record
Abstract
ABSTRACT Tinnitus is a condition in which patients perceive sound without an external stimulus. It can be classified into either pulsatile or nonpulsatile tinnitus. This condition affects around 14% of the global population, and the severity of tinnitus can range from barely noticeable to devastating. In most cases, tinnitus is benign and nonpulsatile in nature. The diagnostic role of imaging is to detect treatable and specific pathology. Therefore, a comprehensive clinical assessment, which includes a meticulous examination for associated symptoms like hearing loss, vertigo, or headaches, along with a thorough physical examination, otoscopy, and audiologic testing, is imperative before considering any imaging studies as the choice of imaging will depend on various factors. Nonpulsatile or continuous tinnitus is most commonly associated with presbycusis but can also be caused by functional injuries due to ototoxic medications or exposure to loud noise and usually requires no imaging evaluation. Unlike nonpulsatile tinnitus, imaging patients with pulsatile tinnitus typically reveals perceptible findings. The cause of pulsatile tinnitus is usually a vascular tumor, vascular malformation, or vascular anomaly. Other causes of tinnitus include idiopathic intracranial hypertension, otosclerosis, Paget’s disease, and Meniere’s disease. One of the main challenges is that the underlying cause of tinnitus is often unknown. Another challenge is that tinnitus can have a significant effect on a person’s quality of life, yet the condition is not life-threatening and there is no cure. We present a clinical review of the most prevalent causes of tinnitus along with an emphasis on the diagnostic imaging workup and management of common presentations.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".