Animal Models of Chronic Obstructive Pulmonary Disease Exacerbations
Bibliographic record
Abstract
Modeling acute exacerbations of chronic obstructive pulmonary disease (AECOPD) in animals has proven challenging due to the clinical and pathological complexity of the underlying disease. This has hindered the progress in understanding the cellular and molecular mechanisms that lie beneath AECOPD. In this chapter, we will address modeling possibilities of AECOPD that may be drawn from the current knowledge of factors that cause exacerbations. Importantly, since it is widely accepted that the most common causes of AECOPD are viral and bacterial infections, animal models of AECOPD should incorporate both the causative agents of exacerbation: viruses and bacteria. However, other factors that are also believed to determine both progression of COPD, as well as the frequency and severity of AECOPD, such as proteolytic enzymes, cigarette smoke or other noxious stimuli must also be considered. Such animal models will provide mechanistic insight into the etiology of AECOPD and will prove invaluable in furthering our understanding of key events in disease pathogenesis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".