Predictors of progression in systemic sclerosis patients with interstitial lung disease
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Systemic sclerosis (SSc) is a systemic autoimmune disease affecting multiple organ systems, including the lungs. Interstitial lung disease (ILD) is the leading cause of death in SSc.There are no valid biomarkers to predict the occurrence of SSc-ILD, although auto-antibodies against anti-topoisomerase I and several inflammatory markers are candidate biomarkers that need further evaluation. Chest auscultation, presence of shortness of breath and pulmonary function testing are important diagnostic tools, but lack sensitivity to detect early ILD. Baseline screening with high-resolution computed tomography (HRCT) is therefore necessary to confirm an SSc-ILD diagnosis. Once diagnosed with SSc-ILD, patients' clinical courses are variable and difficult to predict, although certain patient characteristics and biomarkers are associated with disease progression. It is important to monitor patients with SSc-ILD for signs of disease progression, although there is no consensus about which diagnostic tools to use or how often monitoring should occur. In this article, we review methods used to define and predict disease progression in SSc-ILD.There is no valid definition of SSc-ILD disease progression, but we suggest that either a decline in forced vital capacity (FVC) from baseline of ≥10%, or a decline in FVC of 5-9% in association with a decline in diffusing capacity of the lung for carbon monoxide of ≥15% represents progression. An increase in the radiographic extent of ILD on HRCT imaging would also signify progression. A time period of 1-2 years is generally used for this definition, but a decline over a longer time period may also reflect clinically relevant disease progression.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it