P098 <break /> Esophageal diameter is associated with systemic sclerosis-associated interstitial lung disease severity and progression
Bibliographic record
Abstract
Rationale: Up to 90% of patients with systemic sclerosis (SSc) have gastrointestinal tract involvement, often including severe esophageal dysmotility. Repeated episodes of gastroesophageal reflux may be related to the initiation and progression of interstitial lung disease (ILD). We studied a cohort of systemic sclerosis-associated interstitial lung disease (SSc-ILD) patients to determine if esophageal diameter is associated with SSc-ILD severity, progression, and asymmetry. Methods: Patients with SSc were recruited from a specialized ILD centre. High-resolution computed tomography scans (HRCTs) were objectively scored by a chest radiologist. HRCT variables included fibrosis score, fibrosis asymmetry, esophageal diameter, and presence of hiatal hernia, tracheal debris, and mucoid impaction. Results: 132 patients with SSc-ILD were included. The mean age was 54.5 years, 84% were women, and most had mild-to-moderate lung function impairment. Esophageal diameter was negatively associated with FVC %-predicted (5.2% decrease in FVC per 1cm increase in esophageal diameter, p < 0.0005) and positively associated with HRCT fibrosis score (2.3% increase in fibrosis score per 1cm increase in esophageal diameter, p < 0.0005; Figure 1A). Patients with a hiatal hernia had a 4.5% higher fibrosis score than patients without a hiatal hernia (p < 0.0005). Esophageal diameter predicted worsening HRCT fibrosis score over the subsequent 2 years (p = 0.03; Figure 1B), but not worsening FVC %-predicted (p = 0.16). There was no association between esophageal diameter and asymmetric disease (p = 0.89).
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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 | 0.000 | 0.001 |
| 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) | 0.005 | 0.001 |
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".