Toward understanding patient experience in idiopathic pulmonary fibrosis
Bibliographic record
Abstract
Idiopathic pulmonary fibrosis (IPF) is a progressive fibrotic lung disease which typically presents in the 6th or 7th decade of life with dyspnoea on exertion, cough and fatigue [1]. Based on a recent systematic review [2], global IPF incidence is 3–9 cases per 100 000 per year in Europe and North America with increasing incidence over time. A similar incidence, 9 cases per 100 000, was reported in Canada using a narrow definition of IPF [3]. The age-adjusted mortality rate for IPF ranges from 2 to 10 per 100 000, resulting in an estimated 30 000–60 000 deaths in Europe in 2014 [4]. Despite the large number of individuals impacted by this nominally rare disease, there has been only one intervention proven to increase life expectancy in IPF and that is lung transplantation [5]. The small number of lung transplants available, and the common comorbidities of aging which accompany IPF, make this an inadequate intervention for the majority of patients. Unfortunately, over the past 40 years, multiple clinical trials in IPF have failed to achieve pre-specified primary outcomes. Validation of patient-reported outcomes in IPF is essential in incorporating patient voice in IPF clinical trials
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 | 0.007 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.013 | 0.023 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".