Eosinophilic otitis media diagnosis using flow cytometric immunophenotyping
Bibliographic record
Abstract
OBJECTIVES: (1) To assess the ability of flow cytometric immunophenotyping to detect and quantitate eosinophils in patients with eosinophilic otitis media (EOM). (2) to evaluate the association of EOM to bronchial asthma. METHODS: Twenty-one patients with chronic otorrhea or middle ear effusion (MEE) were included in this prospective cohort study. Group I composed of 10 patients (14 ears) and associated to bronchial asthma. Group II included 11 patients (11 ears) without bronchial asthma. Samples of MEE were sent for flow cytometric analysis at initial presentation. Patients with positive eosinophils on flow cytometric immunophenotyping were analyzed after one-month course of dexamethasone eardrops. RESULTS: EOM was diagnosed in all patients of group I and in three patients of group II. The mean eosinophils percentage was 43.5% and 14.2% for group I and group II, respectively (p = .006). Those patients showed a significant response to dexamethasone eardrops, both on clinical examination and on flow cytometric analysis with a decrease in eosinophil levels post-treatment. However, this improvement was temporary and symptoms recurred after treatment cessation. Bronchial asthma was not associated to all patients with EOM. CONCLUSION: Diagnosis of EOM remained mostly clinical; flow cytometry immunophenotyping of MEE may be helpful as an additional tool in diagnosis and monitoring the response to treatment, particularly in non-asthmatic patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".