Repeatability and validity of cell and fluid‐phase measurements in nasal fluid: a comparison of two methods of nasal lavage
Bibliographic record
Abstract
BACKGROUND: There is little information on the repeatability of cell counts and fluid-phase measurements in nasal fluid obtained by different methods of nasal lavage. OBJECTIVE: To compare the repeatability and validity of total and differential cell counts and eosinophil cationic protein (ECP) in nasal secretions obtained by two methods of nasal lavage. PATIENTS AND METHODS: Twelve healthy subjects and twelve subjects with clinically stable allergic rhinitis were randomly assigned to two nasal lavages (separated by 48 h), by one of two methods in the first week and by the second method in the following week. One method was a modification of the method described by Greiff et al. and Grunberg and coworkers and the other was that described by Naclerio and coworkers. RESULTS: Both methods of nasal lavage gave poorly repeatable eosinophil counts and ECP in normal subjects but better repeatability in subjects with rhinitis. The modified Greiff/Grunberg method gave higher and more repeatable total cell count and, in subjects with rhinitis, more reproducible ECP levels compared with the Naclerio METHOD: Both methods were able to discriminate between healthy and rhinitic subjects: mean +/- SD eosinophil percentage count and eosinophil cationic protein differences were 4.5 +/- 4% (P < 0.05) and 24.5 +/- 46.9 microg/L (P < 0.05), respectively, with the modified method and 7.0 +/- 4% (P < 0.05) and 26.9 +/- 68 microg/L (P < 0.05), respectively, with the Naclerio method. CONCLUSION: Both methods are valid and discriminate between normal and rhinitic subjects. In subjects with rhinitis, although the repeatability of eosinophil counts is similar by both methods, the modified Greiff/Grunberg method gives more reproducible ECP measurements, compared with the Naclerio method.
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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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".