Changes on chest HRCT in systemic sclerosis-related interstitial lung disease after autologous haematopoietic stem cell transplantation
Bibliographic record
Abstract
OBJECTIVE: To evaluate extent of interstitial lung disease (ILD) and oesophageal involvement using high-resolution computed tomography (HRCT) in early diffuse SSc patients after autologous haematopoietic stem cell transplantation (aHSCT). METHODS: Overall chest HRCT, lung function and skin score changes were evaluated in 33 consecutive diffuse SSc patients before and after aHSCT during yearly routine follow-up visits between January 2000 and September 2016. Two independent radiologists blindly assessed the ILD extent using semi-quantitative Goh and Wells method, the widest oesophageal diameter (WOD) and the oesophageal volume (OV) on HRCT. Patients were retrospectively classified as radiological responders or non-responders, based on achieved stability or a decrease of 5% or more of HRCT-ILD at 24 months post-aHSCT. RESULTS: Using a linear mixed model, the regressions of the extent of ILD and of ground glass opacities were significant at 12 months (ILD P = 0.001; ground glass opacities P = 0.0001) and at 24 months (ILD P = 0.007; ground glass opacities P = 0.0008) after aHSCT, with 18 patients classified as radiological responders (probability of response 0.78 [95% CI 0.58, 0.90]). Meanwhile the WOD and the OV increased significantly at 12 months (WOD P = 0.03; OV P = 0.34) and at 24 months (WOD P = 0.002; OV P = 0.007). Kaplan-Meier analyses showed a trend towards better 5-year survival rates (100% vs 60%; hazard ratio 0.23 [95% CI 0.03, 1.62], P = 0.11) among radiological responders vs non-responders at 24 month follow-up after aHSCT. CONCLUSION: Real-world data analysis confirmed significant improvement in extent of HRCT SSc-ILD 24 months after aHSCT, although oesophageal dilatation worsened requiring specific attention.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".