A Kit to Facilitate and Standardize the Processing of Sputum for Measurements of Airway Inflammation
Bibliographic record
Abstract
BACKGROUND: The use of inflammometry has been shown to be effective for managing asthma. However, sputum processing can be time consuming. Furthermore, methods of sputum processing can vary among facilities. To help with standardization and to simplify the procedure for laboratory staff, a novel, commercially available processing device (Accufilter, Cellometrics Inc, Canada) has been developed. OBJECTIVE: To assess the validity of the Accufilter device and kit for recovery of treated specimens, and for quantitative sputum inflammatory cell counts by comparing intrasample measurements with those using the same procedure without the Accufilter device and kit. METHODS: The present study was a wet laboratory comparison of induced sputum cell counts obtained from sputum processed with versus without the device and kit. Comparisons of each sputum specimen were performed by the same technologist in random order. RESULTS: A total of 39 samples were processed using both the standard method and the Accufilter device. The intraclass correlation coefficients were high for the weight of the filtrate, and for eosinophil and neutrophil differential counts. CONCLUSION: A good degree of agreement of results was apparent when the two methods were compared. The differences noted between both methods were minimal and did not modify clinical interpretation. The use of the Accufilter device and kit can be used in place of the standard method for sputum quantitative analysis, especially in centres with large sample loads.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".