Clinical decision rule to predict the presence of interstitial lung disease in systemic sclerosis
Bibliographic record
Abstract
OBJECTIVE: To develop a clinical decision rule to predict the presence of interstitial lung disease (ILD) in systemic sclerosis (SSc; scleroderma) and to estimate the prevalence of SSc-ILD. METHODS: Patient data were extracted from the Canadian Scleroderma Research Group registry. Three algorithms for the clinical decision rule were considered based on lung auscultation, chest radiography (CXR), and % predicted forced vital capacity (FVC). High-resolution computed tomography (HRCT) scans were used as the gold standard to determine the diagnostic properties of the 3 algorithms. Multiple imputation was used to impute HRCT data when missing, thereby avoiding bias due to differential referral for HRCT. RESULTS: This study included 1,168 patients. Of the patients with HRCT scans, 65% had evidence of ILD, compared to 26% by physical examination and 22% by CXR. The FVC of those who did not have HRCT was 8.8% greater than those who did (95% confidence interval [95% CI] 6.0-11.6%). Algorithm A, which identified the presence of ILD based on crackles on lung auscultation and/or findings on CXR, had a likelihood ratio of 3.9, compared to 3.2 for Algorithm B (which included patients with FVC <70%) and 2.2 for Algorithm C (which included patients with FVC <80%). The prevalence of ILD in the cohort was estimated to be 52% (95% CI 46-59%). CONCLUSION: We developed a simple clinical decision rule to predict SSc-ILD with good test characteristics. The prevalence of ILD in a large, unselected SSc cohort was estimated to be 52%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".