Accuracy and Feasibility of Ultrasound in the Diagnosis of Otitis Media in Cats
Bibliographic record
Abstract
BACKGROUND: Otitis media (OM) is diagnosed via imaging or, in some cases, otoscopic evaluation, by detecting fluid in the tympanic bulla (TB). In cats, the bulla septum divides the TB into ventromedial (VMC) and dorsolateral (DLC) compartments, with ultrasound restricted to imaging the VMC only. HYPOTHESIS/OBJECTIVES: To evaluate the accuracy and feasibility of ultrasound in diagnosing naturally occurring OM in cats, using computed tomography (CT) or magnetic resonance imaging (MRI) as the reference standard. ANIMALS: Thirty-two privately owned cats (64 ears) with and without OM were enrolled in the study. MATERIALS AND METHODS: In this cross-sectional study, CT or MRI confirmed fluid (OM) or air (normal) in the TB, while ultrasound imaged the VMC for air or fluid. Performance statistics for ultrasound in diagnosing OM were calculated. RESULTS: Bulla ultrasound took an average of 3.5 min to complete and 23 cats were awake. Ultrasound detected air in the VMC in 41 ears, fluid in 22 ears and acoustic shadowing in one ear owing to TB wall thickening, precluding the detection of gas or fluid. Thirty-nine middle ears were air-filled and 25 ears had fluid based on CT/MRI. Two false negatives resulted from undetectable scant fluid lines. Ultrasonographic data of 63 ears (ear with acoustic shadow was excluded) showed the following: sensitivity (92%), specificity (100%), positive predictive value (100%), negative predictive value (95%) and accuracy (97%). CONCLUSIONS AND CLINICAL RELEVANCE: Ultrasound was rapid, well-tolerated and reliably differentiated fluid from air in the VMC, diagnosing OM in most cats. False negatives arose from scant fluid. Acoustic shadowing may represent chronic OM.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".