Reproducibility of cell counts in nasal lavage: a comparison of pooled versus non-pooled nasal lavage samples
Bibliographic record
Abstract
Nasal lavage is used to collect cells and inflammatory mediators from the nasal cavity. No universal method for nasal lavage exists and there is little evidence as to which method is most reproducible or reflective of tissue inflammation. To compare the reproducibility of a single lavage versus three pooled lavages. Randomized crossover trial of 7 perennial allergic rhinitis, 7 nasal polyp, and 7 control subjects. Two visits with single lavage and two with pooled lavage in alternating order were performed 7 to 10 days apart using a modified Naclerio method. A higher mean cell count was obtained using pooled lavage (means of 342 and 304 vs. 243 and 246, p=0000.4). The mean eosinophil percentage was comparable for both methods (for >100 cell count samples, 7% and 4% for single compared to 5% and 5% for pooled lavage, p=0.2). Single sample lavage produced a higher intraclass correlation (ICC) for eosinophil percentage (0.695 vs. 0.583). A cutoff of 100 total cells gave the most reproducible eosinophil % with ICC of >0.8. The ICC was 0.87 for single sample lavage and 0.81 for pooled lavage with >100 and 0.671 for SSL and 0.535 for MSL with >20 cells. Neutrophil (p=0.2), lymphocyte (p=0.2), monocyte (p=0.3), or basophil (p=0.3) percentages were not significantly different. Although the total cell counts were lower, single sample lavage was comparable in measuring inflammatory cells. The intraclass correlation of the eosinophil percentage in single lavage was higher than pooled lavage perhaps due to a wash out effect from multiple lavages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.041 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".