Plain language summary: Patient journey and treatment patterns in progressive pulmonary fibrosis
Bibliographic record
Abstract
Plain Language SummaryWhat is this summary about?This study aimed to better understand the patient journey and treatment patterns in people living with progressive pulmonary fibrosis (PPF) from five European countries. Results from the study also provided insights into the best approach for diagnosing and treating patients with PPF.What were the results?Overall, 265 doctors provided data on 1,335 patients with PPF in France, Germany, Italy, Spain and the UK.A quarter of patients (25.2%) were in full-time employment. Of those not in full-time employment, about one in five (18.9%) were unable to work full-time due to interstitial lung disease (ILD).There was an average delay of 7.8 months between first symptoms of ILD and when participants consulted a doctor, and another 7.7 months on average to diagnosis of ILD.At the time of data collection, about half of the patients (47.7%) had moderate ILD and almost one in five (19.5%) had lung scarring that was getting worse, also known as progression.Overall, about eight out of ten patients (77.8%) were receiving treatment at the time of this survey. Almost one in six patients (15.6%) had not been prescribed treatment for ILD in the past.What do the results mean?This real-world study found delays in diagnosis and treatment gaps experienced by patients with PPF in Europe. Because this disease gets worse over time, delays in diagnosis may lead to poor outcomes, including shorter survival time.How to say…Corticosteroid: kaw-tuh-ko-stuh-roydIdiopathic pulmonary fibrosis: id-ee-uh-PATH-ik PUHL-muh-ner-ee fai-BROH-sisImmunosuppressive: i-myoo-no-suh-PREH-suhvInterstitial lung disease: in-tur-STISH-ul luhng dih-ZEEZMycophenolate: my-koe-PHEH-no-laytNintedanib: nin-TED-a-nibPirfenidone: pir-FEN-i-donePrednisone: PRED-nuh-zownProgressive pulmonary fibrosis: pruh-GREH-suhv PUHL-muh-ner-ee-fai-BROH-sisPulmonologist: PUHL-muh-no-lo-gistRheumatoid: roo-muh-toydRheumatologist: roo-ma-to-lo-gistProgressive pulmonary fibrosis (PPF): Lung scarring that gets worse over time, with a negative impact on lung function, quality of life and survival.Pulmonary fibrosis: A lifelong disease where lung tissue is damaged and/or scarred, causing difficulty breathing.This is an abstract of the Plain Language Summary of Publication article.View the full Plain Language Summary PDF of this article to read the full-textLink to original article here
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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 | 0.003 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 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".