Nasal Cytology in the Diagnosis and Treatment of Sinusitis in Atopic and Nonatopic Children
Bibliographic record
Abstract
OBJECTIVES: To investigate the value of nasal cytology in the diagnosis of sinusitis and follow-up of atopic and nonatopic children before and after treatment and to compare the nasal cytologic findings with the radiologic findings. DESIGN: Open randomized investigation. SETTING: Ministry of Health, Ankara Diskapi Children's Hospital. METHOD: Fifty-five children with bronchial asthma and/or allergic rhinitis followed by the Allergy Department of the Ministry of Health, Ankara Diskapi Children's Education and Research Hospital and 35 control children were evaluated for the following parameters: symptoms and signs of sinusitis, total serum immunoglobulin E level and eosinophil count, skinprick tests to common allergens, paranasal sinus radiographs, and nasal cytology (by the Rhinoprobe [Synbiotics Inc. London] method and wax paper blow). MAIN OUTCOME MEASURES: Nasal cytology, radiologic findings, and treatment. RESULTS: There were no significant correlations between radiologic and cytologic findings in any of the groups (p > .05). In both atopic and nonatopic chronic sinusitis patients, the Rhinoprobe method had results similar to the radiologic findings, and there was a significant relationship (p < .05). CONCLUSIONS: Nasal cytology is still a diagnostic tool in the follow-up and evaluation of chronic sinusitis in atopic children but should not be considered an adequate alternative to sinus radiography.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".