Bibliographic record
Abstract
Smoking is the single most important causal risk factor for COPD. The public health campaigns and policies have worked to reduce smoking rates by over 50% since those of the 1970s and are now at an all-time low in the UK,1 Canada and throughout most of the western world. Yet, curiously and paradoxically, the burden of COPD (as measured by its prevalence, hospitalisation and mortality rate) is at an all-time high in these countries, and over the next 20 years, the burden of COPD is expected to more than double.2 Why? The answer is a simple math issue related to ageing. Most industrialised countries of the world are getting older and, unquestionably, COPD is an age-related disorder. Whereas COPD is almost unheard of in individuals less than 40 years of age (even among heavy smokers), 1 in 10 lifetime never-smokers and 1 in 3 develop COPD by age 75.3 This notion of accelerated ageing of COPD is supported by animal models, which demonstrate premature appearance of emphysematous lungs in mice, which have genetically altered age-related pathways (eg, Klotho mice).4 Physiologically, ageing can be defined as the progressive decline in homeostasis and loss of tissue and organ functions over time.5 Ageing is a risk factor for many (non-communicable) chronic conditions beyond COPD including cancer, congestive heart failure, dementia, diabetes and osteoarthritis.5 The aetiology of ageing is unknown, but the most popular theory involves accumulation of reactive oxygen species (ROS) related to excess oxidative stress.6 At a cellular level, ageing manifests as an irreversible loss of proliferative capacity of viable mitotic cells, termed cellular senescence. Unlike apoptotic cells, senescent cells remain metabolically active and display a senescence-associated secretory phenotype characterised by the production of proinflammatory cytokines such as interleukin (IL)-6, IL-8 and matrix metalloproteinases (MMPs).7 …
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".