Bibliographic record
Abstract
There is a very large number of experimental approaches that prevent cigarette smoke-induced emphysema in laboratory animals, but the few similar treatments that have been tried in humans have had minimal effects, leading to questions of whether animal models of chronic obstructive pulmonary disease (COPD) are of any use in developing treatments for human disease. We review possible reasons for this problem. First, humans usually get treated when they have severe (Global Initiative for Chronic Obstructive Lung Disease III/IV) COPD, but animal models only produce mild (Global Initiative for Chronic Obstructive Lung Disease I/II) disease that never progresses after smoking cessation, and never develops spontaneous exacerbations (i.e., animal models are not models of severe human disease, and probably can't be used to model treatment of severe disease). Second, animal models have concentrated on emphysema and largely ignored small airway remodeling, but small airway remodeling is an equally important cause of airflow obstruction. In addition, small airway remodeling and emphysema are independent responses to smoke, and some experimental animal treatments prevent both lesions, but many do not. Third, animal models are typically Day 1 of smoke exposure "prevention" models, but humans are always treated well along in the course of their disease; thus, any human treatment will be an intervention, and not a prevention. We propose that animal models should examine both emphysema and small airway remodeling, and that experiments should include a relatively late intervention arm. This approach, combined with the realization that human COPD probably needs early rather than late treatment, may make development of treatments based on animal models more relevant.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.027 | 0.019 |
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".