Endoscopic scoring of mucus quantity and quality: observer and horse variance and relationship to inflammation, mucus viscoelasticity and volume
Bibliographic record
Abstract
REASONS FOR PERFORMING STUDY: Endoscopic scoring of airway mucus quantity and quality has not been critically assessed. OBJECTIVES: To evaluate mucus scores for 1) observer- and horse-related variance and 2) association with inflammation, mucus viscoelasticity and measured volume. METHODS: Variance of scoring within and between observers and over time within horses were determined for airway mucus accumulation, apparent viscosity, localisation and colour, and correlations of mucus accumulation scores with neutrophil ratios in secretions. The relationship of accumulation score to measured volumes of 'artificial mucus' was investigated. Correlations of mucus accumulation, apparent viscosity and colour scores with measured viscoelasticity were tested. Viscoelasticity was compared between tracheal secretion samples collected ventrally and dorsally. RESULTS: Mucus accumulation scoring showed excellent interobserver agreement and moderate horse-related variance, was related to measured volumes of 'artificial mucus', and correlated well with neutrophilic airway inflammation. Scores of mucus viscosity, colour and localisation showed high observer-related variance. Mucus accumulation, apparent viscosity and colour scores did not correlate with measured tracheal mucus viscoelasticity, but dorsally-localised mucus showed 2-fold higher measured viscoelasticity than ventrally-localised samples. CONCLUSIONS: Mucus accumulation scores are a reproducible measure of mucus volumes in the trachea. POTENTIAL RELEVANCE: Endoscopic scoring of mucus accumulation is a reliable clinical and research tool. In contrast, apparent viscosity, localisation and colour scores should be interpreted with caution.
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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 | 0.067 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| 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".