Bibliographic record
Abstract
Investigations toward the understanding of chronic obstructive pulmonary disease (COPD) have been directed, so far, to the study of mechanisms leading to the disease. We believe that understanding why ~80% of smokers evade COPD and how this evasion is accomplished might be a fruitful endeavour that could advance knowledge of the development of the disease. Since the inflammatory infiltrate smokers develop seems to be the key element leading to the lung destruction in COPD, the understanding of the possible ways inflammation can be dampened, as well as its consequences, ought to be important. We review here some of the mechanisms by which inflammation is controlled: by the post-translational regulons, by the mechanisms preventing full activation of dendritic cells and by the regulatory T-cells. The potential role of the M2 alveolar macrophage phenotype and the newly described myeloid-derived suppressor cells is mentioned. We also point out that evasion comes at a price, as healthy smokers might be immunosuppressed to some extent and unable to prevent the development of cancer, certainly less so than in severe COPD, where immunity is heightened. Probably, the knowledge of the mechanisms of evasion from COPD could add significantly to the understanding of those leading to the disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".