Alcohol Fixation of Induced Sputum Samples for Applications in Rural Communities
Bibliographic record
Abstract
BACKGROUND: Sputum induction is a tool recommended for the assessment of airway inflammation and disease management. Currently, its use is limited because samples need to be processed within 3 h of induction (ie, while cells are viable); therefore, this procedure is unavailable to most clinicians. OBJECTIVE: To develop a fixation method for induced sputum samples that allows for a delay in processing while maintaining sample integrity and not altering the standard processing method. METHODS: Sputum samples were collected and split into three portions: a fresh sample processed using the routine method (within 3 h, using dithiothreitol); fixation in alcohol followed by delayed processing using the routine method (within 48 h to 72 h, using dithiothreitol); and fixation in formaldehyde followed by delayed processing using an alternative method (within 48 h to 72 h, using proteolysis). For each method, cytospins were prepared and differential cell counts were performed. RESULTS: Fixation in alcohol provides accurate measures of eosinophils and macrophages, but not neutrophils. Formaldehyde fixation provides accurate measures of neutrophils and macrophages, but not eosinophils. DISCUSSION: Alcohol fixation is a superior method for eosinophil quantification. It requires alteration of standardized methods for sputum sample processing and should be recommended for monitoring eosinophilic airway disease in settings where immediate processing of a sputum sample is not possible.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".