The Efficiency of Sputum Cell Counts in Cystic Fibrosis
Bibliographic record
Abstract
BACKGROUND: Technical factors relating to processing viscid sputum in cystic fibrosis (CF) and their influence on the reproducibility and validity of cell counts need to be evaluated. In addition, the methods need to be standardized so that they can be applied clinically and in research. OBJECTIVE: To examine the efficiency, reliability and validity of processing small volumes of spontaneously expectorated sputum from subjects with CF. METHODS: Sputum was collected from adults with CF (n=35) and compared with sputum from adults with infective bronchitis or bronchiectasis (IB/B) (n=16), or with asthma or chronic obstructive pulmonary disease (AS/COPD) (n=25). Selected sputum (100 mg to 200 mg) was processed with dithiothreitol (0.1%) and filtered. Total cell count (TCC) and viability were obtained in a counting chamber and cytospins were prepared and stained with Wright's for a differential cell count. Sputum and filter remnant were processed for TCC, viability and differential cell count, and the efficiency was determined by comparing the mean loss in cell yield to the filter. Two different portions from the same sputum sample were processed for cell counts to determine reproducibility. Results were compared with those from IB/B and AS/COPD groups. RESULTS: Efficiency of cell dispersal was excellent and similar to that in AS/COPD and IB/B groups. Reproducibility of cell counts from two portions of a sputum sample was high (>or=0.80). CF sputum demonstrated a raised TCC and neutrophilia similar to IB/B but significantly higher than AS/COPD. CONCLUSION: The selection method of evaluating cell counts in viscid CF sputum is efficient, reproducible and valid.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".