New and Controversial Therapies for Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
There is a medical need in chronic obstructive pulmonary disease (COPD) for novel therapies with improved anti-inflammatory activity and the potential to affect disease progression. The concept of "super bronchodilators" is currently in development. Nonsteroidal antiinflammatory approaches are now being developed and are going under investigation. Restoration of steroid sensitivity is also a potential target in COPD. This could be done by increasing HDAC-2 expression and/or activation, by decreasing p38-kinase or delta-isoform of PI3K. Disease modification that may impact on long-term health status and survival of the patient is the primary and most challenging target for novel therapy in COPD. There is need, however, for new methods and biomarkers to follow disease progression. There has been significant innovation in the nonpharmacological management of COPD including team-based approaches with collaborative self-management. Information and guidelines about self-management, coupled with interventions that provide patients with needed skills to increase their confidence, have potential impact on patient health. Recently, it has been shown that self-management programs reduce the probability of COPD hospital admissions. We should certainly be enthusiastic about these results, but there is need to increase our understanding of the relative effectiveness of specific components and how to best support COPD patients in self-management. Conceptualization of a research plan for COPD self-management is needed for future research to be successful.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".