Reproducibility of Sputum Differential Cell Counts Is not Affected by Squamous Epithelial Cells
Bibliographic record
Abstract
OBJECTIVE: Induced sputum is used to assess markers of inflammation in asthmatic individuals, and sputum cell differential counts provide an outcome to evaluate the presence, type, and degree of inflammation in the airways. Contamination of sputum slides with squamous epithelial cells (SECs) has been reported to adversely affect the reproducibility of sputum cell differential counts; however, this has not been studied in a controlled manner. Excluding sputum slides because of excessive squamous cell contamination can be problematic resulting in under-powering of studies. The aim of this study is to evaluate the effect of SEC contamination and cell dispersion on the reproducibility of differential counts of sputum cells prepared on glass slides. STUDY DESIGN: A total of 33 sputum samples were induced from 11 subjects with mild asthma under baseline conditions and following an allergen inhalation challenge. Mucoid and salivary portions of each sample were divided and processed in parallel. To evaluate the effect of increasing the proportion of SEC and to evaluate the effect of increasing the number of leukocytes per high power field (HPF), four slides with varying leukocyte numbers and SEC percentages were prepared from each sample by combining and adjusting the volume of cell suspensions derived from mucous and saliva. The four slides were prepared to fall in the following categories: (A) 50 cells/HPF and <20% SEC; (B) 50 cells/HPF and >20% SEC; (C) 100 cells/HPF and <20% SEC; and (D) 100 cells/HPF and >20% SEC. All slides were blinded and counted twice by an experienced observer, and twice by an inexperienced observer. RESULTS: The differential cell counts for eosinophils, macrophages, and neutrophils were highly reproducible under all conditions when enumerated by an experienced observer (ICC > 0.9), and furthermore, SEC contamination did not affect ICC when differential counts were enumerated by an inexperienced observer (ICC > 0.8). CONCLUSION: Our results demonstrate that slides containing SECs, up to 40% in this study, have reproducible differential cell counts.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".