Histogram-Based Densitometry Index to Assess the Severity of Interstitial Lung Disease in Systemic Sclerosis in Standard and Low-Dose Computed Tomography
Bibliographic record
Abstract
Objective Mean lung attenuation, skewness, and kurtosis are histogram-based densitometry variables that quantify systemic sclerosis–associated interstitial lung disease (SSc-ILD) and were recently merged into a computerized integrated index (CII). Our work tested the CII in low-dose 9-slice (reduced) and standard high-resolution computed tomography (CT) scans to evaluate extensive SSc-ILD and predict mortality. Methods CT scans from patients with SSc-ILD were assessed using the software Horos to compute standard and reduced CIIs. Extensive ILD was determined following the Goh staging system. The association between CIIs and extensive ILD was analyzed with a generalized estimating equation regression model, the predictive ability of CIIs by the area under the receiver-operation characteristic curve (AUC), and the association between CIIs and death by Kaplan-Meier analysis. Results Among 243 patients with standard and reduced CT scans available, 157 CT scans from 119 patients with SSc-ILD constituted the derivation cohort. The validation cohort included 116 standard and 175 reduced CT scans. Both CIIs from standard (odds ratio [OR] 0.53, 95% CI 0.37-0.75; AUC 0.77, 95% CI 0.68-0.87) and reduced CT scans (OR 0.54, 95% CI 0.35-0.82; AUC 0.78, 95% CI 0.70-0.87) were significantly associated with extensive ILD. A threshold of CII ≤ −0.96 for standard CT scans and CII ≤ −1.85 for reduced CT scans detected extensive ILD with high sensitivity in both derivation and validation cohorts. Extensive ILD according to Goh staging (OR 2.94, 95% CI 1.10-7.82) and standard CII ≤ −0.96 (OR 1.78, 95% CI 1.24-2.56) significantly predicted mortality; a marginalPvalue was observed for reduced CII ≤ −1.85 (OR 1.27, 95% CI 0.93-1.75). Conclusion Thresholds for both standard and reduced CII to identify extensive ILD were developed and validated, with an additional association with mortality. CIIs might help in clinical practice when radiology expertise is missing.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".