A global registry for idiopathic pulmonary fibrosis: the time is now
Bibliographic record
Abstract
Idiopathic pulmonary fibrosis (IPF) is a chronic progressive interstitial lung disease (ILD) of unknown aetiology with a median survival of only 3 years [1]. As a rare and orphan disease, IPF is difficult to study even within specialised referral centres and regional collaborative networks [2]. Although some local and regional prospective IPF registries exist, they generally do not share methodology and are not easily combined. A global IPF registry would facilitate a better understanding of disease biology and behaviour [3], as well as identification of regional variations in diagnostic and management patterns that will help establish best practices [4]. Moreover, a global IPF registry would create a network for more efficient and affordable clinical trial conduct [5–7], and provide a platform for collaboration among stakeholders (patients, caregivers, clinicians, researchers, professional and scientific societies, industry partners, and governments). We believe it is time to actively develop a global registry for IPF patients. The disease and the patients who suffer from it cannot wait. We propose a 5-year plan to achieve this goal (fig. 1). Figure 1– Major steps and the timeline for the development of a global idiopathic pulmonary fibrosis (IPF) registry. It …
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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.014 | 0.025 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".