Fine crackles on chest auscultation in the early diagnosis of idiopathic pulmonary fibrosis: a prospective cohort study
Bibliographic record
Abstract
INTRODUCTION: Idiopathic pulmonary fibrosis (IPF) is an interstitial lung disease (ILD) with a poor prognosis. Early diagnosis and treatment of IPF may increase lifespan and preserve quality of life. Chest CT is the best test to diagnose IPF, but it is expensive and impractical as a screening test. Fine crackles on chest auscultation may be the only best to screen for IPF. METHODS: We prospectively assessed the presence and type of crackles on chest auscultation in all patients referred to the ILD Clinic at the Kingston Health Sciences Center in Ontario, Canada. Clinicians with varying levels of experience recorded the presence of fine crackles, coarse crackles or both independently and unaware of the final diagnosis. We applied multinomial logistic regression to adjust for ILD severity and factors that could affect the identification of crackles. RESULTS: We evaluated 290 patients referred to the ILD Clinic. On initial presentation, 93% of patients with IPF and 73% of patients with non-IPF ILD had fine crackles on auscultation. In patients with IPF, fine crackles were more common than cough (86%), dyspnoea (80%), low diffusing capacity (87%), total lung capacity (57%) and forced vital capacity (50%). There was 90% observer agreement in identifying fine crackles at a subsequent visit. In multiple regression analysis, the identification of fine crackles was unaffected by lung function, symptoms, emphysema, chronic obstructive pulmonary disease, obesity or clinician experience (p>0.05). CONCLUSIONS: Fine crackles on chest auscultation are a sensitive and robust screening tool that can lead to early diagnosis and treatment of patients with IPF.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".