Association of Computed Tomography Densitometry with Disease Severity, Functional Decline, and Survival in Systemic Sclerosis-associated Interstitial Lung Disease
Bibliographic record
Abstract
Abstract Rationale Measuring disease extent and progression of systemic sclerosis–associated interstitial lung disease (SSc-ILD) is challenging, with recent studies suggesting potential utility of quantitative measurements from computed tomography (CT) scans. Objectives To determine the associations of quantitative computed tomography (qCT) density-based measures with physiological parameters, visual CT scores, and survival in patients with SSc-ILD. Methods Patients with SSc-ILD and volumetric high-resolution CT images with ≤1.25-mm slice thickness were retrospectively identified. Cardiothoracic radiologists produced visual CT scores of ground glass, reticulation, and honeycombing, with visual fibrosis score equaling the sum of reticulation and honeycombing. qCT measurements included high-attenuation areas (HAA), skewness, kurtosis, and mean lung attenuation (MLA). Associations of qCT measures with pulmonary physiology, visual CT scores, and mortality were analyzed using Spearman’s rank correlation and Cox regression. Results A total of 503 CT scans from 170 patients with SSc-ILD were included. qCT HAA, skewness, kurtosis, and MLA were associated with lung function and visual fibrosis scores, independent of age, sex, and pack-years, using both baseline and change data. Baseline and changes in qCT measures (except ∆skewness) were associated with mortality on unadjusted analysis. Changes in all qCT variables remained associated with survival after adjustment for baseline age, sex, pack-years, and lung function, but not when adjusting for changes in lung function. ∆HAA and ∆MLA were associated with survival after adjustment for age, sex, pack-years, and change in visual CT scores. Conclusions CT density measurements correlate with physiologic impairment and visual CT scores in patients with SSc-ILD; however, they were not associated with survival independent of changes in pulmonary physiology. The clinical utility of more sophisticated qCT measures should be explored.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".