Bibliographic record
Abstract
Blair G Fulton,1 Christopher J Ryerson1,2 1Department of Medicine, 2Centre for Heart Lung Innovation, University of British Columbia, Vancouver, BC, Canada Abstract: Major risk factors for idiopathic pulmonary fibrosis (IPF) include older age and a history of smoking, which predispose to several pulmonary and extra-pulmonary diseases. IPF can be associated with additional comorbidities through other mechanisms as either a cause or a consequence of these diseases. We review the literature regarding the management of common pulmonary and extra-pulmonary comorbidities, including chronic obstructive pulmonary disease, lung cancer, pulmonary hypertension, venous thromboembolism, sleep-disordered breathing, gastroesophageal reflux disease, coronary artery disease, depression and anxiety, and deconditioning. Recent studies have provided some guidance on the management of these diseases in IPF; however, most treatment recommendations are extrapolated from studies of non-IPF patients. Additional studies are required to more accurately determine the clinical features of these comorbidities in patients with IPF and to evaluate conventional treatments and management strategies that are beneficial in non-IPF populations. Keywords: interstitial lung disease, management, idiopathic pulmonary fibrosis, comorbidities
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".