Opportunities to diagnose fibrotic lung diseases in routine care: A primary care cohort study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Temporal trends of healthcare use in the period before a diagnosis of pulmonary fibrosis are poorly understood. We investigated trends in respiratory symptoms and LR HRU in the 10 years prior to diagnosis. METHODS: We analysed a primary care clinical cohort database (UK OPCRD) and assessed patients aged ≥40 years who had an electronically coded diagnosis of pulmonary fibrosis between 2005 and 2015 and a minimum 2 years of continuous medical records prior to diagnosis. Exclusion criteria consisted of electronic codes for recognized causes of pulmonary fibrosis such as CTD, sarcoidosis or EAA. RESULTS: Data for 2223 patients were assessed. Over the 10 years prior to diagnosis of pulmonary fibrosis, there was a progressive increase in HRU across multiple LR-related domains. Five years before diagnosis, 18% of patients had multiple healthcare contacts for LR complaints; this increased to 79% in the year before diagnosis, with 38% of patients having five or more healthcare contacts. CONCLUSION: There are opportunities to diagnose pulmonary fibrosis at an earlier stage; research into case-finding algorithms and strategies to educate primary care physicians is required.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".