Demographic and Clinical Factors Associated With Diagnostic Confidence in Interstitial Lung Disease
Bibliographic record
Abstract
Background: Accurate diagnosis of interstitial lung disease (ILD) can be challenging. Accordingly, clinicians may attribute a diagnostic certainty based on guideline criteria and clinical judgment. However, further research is needed to refine this approach and improve diagnostic clarity. Research Question: What are the real-world factors associated with diagnostic confidence in fibrotic ILD? Study Design and Methods: Data were included from all patients enrolled in the Pulmonary Fibrosis Foundation Patient Registry from March 2016 to August 2018. Baseline demographic and clinical characteristics were collected at enrollment, or at the test date closest to the date of consent for longitudinal measures. Descriptive analyses were performed separately for all participants, and for subgroup participants with idiopathic pulmonary fibrosis (IPF) and participants with non-IPF ILD, stratified by the level of investigator diagnostic confidence (high vs medium/low) assigned at registry enrollment. Adjusted ORs and 95% CIs were calculated using multivariable logistic regression, with the aforementioned characteristics as predictors. Results: Data up to April 2022 from 1,992 participants were included. In adjusted logistic regression analyses among all participants, antifibrotic use (OR, 1.51; 95% CI, 1.09-2.07), longer time since diagnosis (OR, 0.94; 95% CI, 0.89-0.98) at the research unit of 365 days, and diabetes (OR, 2.56; 95% CI, 1.01-6.44) were significantly associated with higher diagnostic confidence, and non-IPF idiopathic interstitial pneumonia (vs IPF; OR, 0.36; 95% CI, 0.24-0.55), insurance - other (OR, 0.65; 95% CI, 0.43-0.97), and Hispanic ethnicity (OR, 0.54; 95% CI, 0.31-0.94) were significantly associated with lower diagnostic confidence. Factors associated with diagnostic confidence in the IPF and/or non-IPF ILD groups included age, male sex, region, immunomodulatory medication use, multidisciplinary team discussion, surgical lung biopsy, and definite high-resolution CT pattern. Interpretation: These findings suggest that certain demographic and clinical factors may influence physicians' confidence in diagnosis of IPF and non-IPF ILD. Tailored physician education may help to reduce biases and improve consistency in diagnosis.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".