Comment on: Anti-acid therapy in SSc-associated interstitial lung disease: long-term outcomes from the German Network for Systemic Sclerosis
Bibliographic record
Abstract
Dear Editor, We read with great interest the article by Kreuter et al. [1] which showed that proton pump inhibitors (PPI) improved the 5-year overall survival (OS) by >20% points (OS 70.9% in the non-PPI group and 91.4% in the PPI group) in patients with scleroderma-related interstitial lung disease (SSc-ILD). Also, they reported that PPI improved the 5-year progression-free survival (PFS)—defined as time to either death or ≥10% decrease in predicted forced vital capacity (FVC pred) or ≥15% in predicted carbon monoxide diffusing capacity (Dlco pred)—by >20% points in SSc-ILD patients (PFS 45.9% in the non-PPI group and 66.8% in the PPI group). These clinically and statistically significant improvements in OS and PFS (P <0.0001 for both) occurred despite patients in the PPI group having worse disease, with lower FVC pred, Dlco pred and higher use of immunosuppressants. Interestingly, the authors reported that 37% (415/1117) of patients on PPI in the study had no clinical evidence of gastroesophageal reflux disease (GERD). The 2022 idiopathic pulmonary fibrosis (IPF) and progressive pulmonary fibrosis guidelines [2]—based on limited available evidence—suggested that antacid medication may be appropriate for patients with IPF and symptoms of GERD for the purpose of improving gastroesophageal reflux–related outcomes, as there was no evidence of benefit with PPI on disease progression or death [3–5].
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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 | 0.005 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.016 | 0.010 |
| Insufficient payload (model declined to judge) | 0.054 | 0.029 |
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".