Reproducibility of neutrophil percentages in pooled and non-pooled nasal lavage samples
Bibliographic record
Abstract
Neutrophils form a host’s main immediate immune response. COPD, ozone, infection and other factors have been shown to increase neutrophils found in blood, sputum and nasal lavage. Nasal lavage is used to collect cells and inflammatory mediators from the nasal cavity, and can be used quantify neutrophilic response to inhaled stimuli. To compare the reproducibility of neutrophil percentage in a single sample lavage, SSL, versus multiple sample lavage, MSL (3 lavages performed 15 minutes apart and pooled). Randomized crossover trial of nasal lavage performed on 4 visits 7-10 days apart, alternating between SSL and MSL done in 7 subjects with perennial allergic rhinitis, 7 with bilateral nasal polyposis and 7 controls. The mean (±SEM) neutrophil percentage was not significantly different between the two methods (80 ±12 for SSL, 86 ±8 for MSL, p=0.2). At a minimum total cell count (TCC) cutoff of ≥20, the neutrophil percentage intraclass correlation (ICC) was similar but not sufficiently reproducible for SSL at 0.588 compared to MSL at 0.641. For samples with TCC≥100, the neutrophil percentage ICC of SSL was 0.93 (excellent correlation) and that of MSL was 0.676 (satisfactory). The evaluable samples for TCC≥100 were 60/84 for SSL and 67/84 for MSL. We previously demonstrated in the same experiment that a minimal TCC cutoff of ≥100 cells gave excellent eosinophil percentage ICC for both methods (>0.8). SSL ICC was superior to MSL in measuring nasal lavage neutrophil percentage. This method could be used to assess the effects of inhalant stimuli on the nasal cavity.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".