Reproducibility, Validity, and Responsiveness of Cell Counts in Blown Nasal Secretions
Bibliographic record
Abstract
Cell counts in nasal secretions are not used in routine clinical practice to decide on anti-inflammatory or antimicrobial therapy. This study investigated the reproducibility, reliability (validity), and responsiveness of cell counts in blown nasal secretions with a view to implementing this in routine clinical practice. Nasal secretions were obtained from 19 subjects with allergic rhinitis on 3 days in random order (each separated by 1-2 days) by spontaneously blowing their noses (on 2 days) and by a nasal lavage by the modified Grunberg method on the 3rd day. Total and differential cell counts were performed after dispersing the solutions with dithiothreitol as described previously. At the end of the study, subjects had 1 week of open label treatment with nasal corticosteroids if they had nasal eosinophilia or an antibiotic if they had nasal neutrophilia. If the cell counts were normal, they were not treated. The proportion of eosinophil (%) was highly reproducible (intraclass correlation coefficient [ICC], 0.93), and the total cell count (×106/g) and the proportion of neutrophil (%) were modestly reproducible in blown nasal secretions (ICC, 0.46 and 0.55, respectively). The total cell count was consistently and significantly higher in the blown nasal secretions. The proportion of eosinophils (R(s) = 0.4; p < 0.05) and neutrophils (R(s) = 0.6; p < 0.05) showed modest correlation in the two types of samples. The responsiveness index for eosinophil count was 4.0 and for neutrophil count was 1.5. Total and differential cell counts can be reliably and reproducibly obtained from spontaneously blown nasal secretions. The cell counts are responsive to treatment and can help identify allergic and infective rhinosinusitis and guide therapy and are easy to implement in routine clinical practice.
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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 | 0.023 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".