Bibliographic record
Abstract
The clinical management of idiopathic pulmonary fibrosis (IPF) is challenging. For patients with a progressive disease with no known cure, realistic goals include slowing the rate of disease progression, optimising comorbidities and functional status, managing symptoms, and preventing what is preventable. The latter is short however, mainly including smoking cessation and infection prevention measures. Despite adherence to recommended management, most patients deteriorate over time, with some experiencing acute exacerbation (AE), and the majority dying due to their IPF.1 Identifying modifiable risk factors that limit disease progression or death could change clinical practice, offering preventative tools with which to achieve the goal of improving patient quality and quantity of life. Air pollution exposure is ubiquitous and a well-established risk factor for a wide range of adverse health outcomes including cardiovascular disease and all-cause mortality.2–4 Arguably the most common target is the respiratory system, given its exposure to the inhaled environment. Indeed, air pollution exposures are associated with increased risk of developing and exacerbating airway diseases, bronchiolitis obliterans post lung transplant, as well as being diagnosed with and dying from lung cancer.5–11 IPF was a relative latecomer to the world of air pollution epidemiology, although a number of plausible mechanisms exist to suggest a relationship.12 While it is unlikely that air pollution is the sole driver of disease, it may represent one form of environmental insult that precipitates or accelerates fibrosis in an already vulnerable lung. In Thorax , Sese and colleagues contribute novel data to a growing body of literature …
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".