Magnetic Resonance Imaging of the Mastoid Cavity and Middle Ear: Prevalence and Clinical Significance of Incidental Abnormal Findings in a Nonotolaryngologic Adult and Pediatric Population
Bibliographic record
Abstract
OBJECTIVE: The purposes of this study were to determine the prevalence of abnormalities in the mastoid cavity and middle ear in a nonotolaryngologic population and to correlate the results with clinical data. DESIGN: Prospective, cross-sectional study. SETTING: An academic tertiary care centre. METHODS: We evaluated 100 adults and 30 children from May to July 2003. Patients who had a history of mastoid or middle ear surgery or were presently suffering from otitis media were excluded. Magnetic resonance imaging (MRI) was conducted for the suspected intracranial pathology. MAIN OUTCOME MEASURES: The T2-weighted image was reviewed. The abnormality detected by MRI was divided into (1) mastoid cavity abnormality and (2) middle ear abnormality. All patients were asked to complete a questionnaire pertaining to the symptoms of the mastoid or middle ear pathology and the history of the otitis media. Also, their ears were examined carefully by an otoscope or otomicroscope. RESULTS: In both groups, most of the abnormalities were found in the mastoid cavity. Analysis of the clinical data revealed that abnormal MRI findings of the mastoid cavity were significantly correlated to clinically significant mastoid or middle ear disease in adults. CONCLUSIONS: Incidental MRI abnormalities in the mastoid cavity and middle ear detected in a nonotolaryngologic population were relatively uncommon compared with incidental paranasal sinus abnormalities. However, clinicians should remember the possibility of the pathologies that demand active treatment among these abnormalities, especially when a high signal abnormality is found in the mastoid cavity of an adult.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".