Introduction to precision medicine in COPD
Bibliographic record
Abstract
Although there has been tremendous growth in our understanding of chronic obstructive pulmonary disease (COPD) and its pathophysiology over the past few decades, the pace of therapeutic innovation has been extremely slow. COPD is now widely accepted as a heterogeneous condition with multiple phenotypes and endotypes. Thus, there is a pressing need for COPD care to move from the current "one-size-fits-all" approach to a precision medicine approach that takes into account individual patient variability in genes, environment and lifestyle. Precision medicine is enabled by biomarkers that can: 1) accurately identify subgroups of patients who are most likely to benefit from therapeutics and those who will only experience harm (predictive biomarkers); 2) predict therapeutic responses to drugs at an individual level (response biomarkers); and 3) segregate patients who are at risk of poor outcomes from those who have relatively stable disease (prognostic biomarkers). In this essay, we will discuss the current concept of precision medicine and its relevance for COPD and explore ways to implement precision medicine for millions of patients across the world with COPD.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".