Factor analysis identifies three separate symptom clusters in idiopathic pulmonary fibrosis
Bibliographic record
Abstract
Background Idiopathic pulmonary fibrosis (IPF) is a severe and progressive lung disease with a poor prognosis. Patients with IPF suffer from a high symptom burden, which impairs their health-related quality of life (HRQoL). Lack of research on IPF symptoms and their clustering, however, makes symptom-centred care challenging. Methods We sent two questionnaires, RAND 36-Item Health Survey and Edmonton Symptom Assessment System, to 300 patients from the FinnishIPF registry. Of the 300 patients, 245 (82%) responded. We performed an exploratory factor analysis on the results to search for potential clustering of symptoms into factors. Results We found three distinct symptom factors: the emotional factor (including depression, anxiety, insomnia, loss of appetite and nausea), the pain factor (pain at rest or in movement) and the respiratory symptoms factor (shortness of breath, cough, tiredness and loss of wellbeing). Correlation was strong within the factors (ρτ0.78–0.85) and also evident between them. The factors correlated with the different dimensions of HRQoL: the emotional factor with mental health (correlation coefficient=−0.69) and vitality (−0.63), the pain factor with bodily pain (−0.72) and the respiratory symptoms factor with vitality (−0.69), general health (−0.64) and physical functioning (−0.62). Conclusion We found three distinct symptom factors in IPF, of which respiratory and emotional factors showed the strongest association with decreasing HRQoL. Routine assessment of IPF patients' respiratory symptoms, mental health and pain are important as these may be linked with other symptoms and significantly impair the patient's HRQoL.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".