The Effect of <scp>Anti‐Scl</scp>‐70 Antibody Determination Method on Its Predictive Significance for Interstitial Lung Disease Progression in Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to assess the predictive significance of anti-Scl-70 (anti-topoisomerase I) antibodies, as determined by three different methods, for decline in forced vital capacity (FVC) within the first year of follow-up in patients with systemic sclerosis (SSc)-related interstitial lung disease (ILD). METHODS: Patients in the Genetics Versus Environment in Scleroderma Outcome Study cohort who had ILD (verified by imaging) and available FVC% at enrollment, plus 12 to 18 months thereafter, were examined. All patients had a disease duration of 5 years or less at enrollment. The annualized percentage change in FVC% at 1 year follow-up was the outcome variable. Anti-Scl-70 antibodies were determined by passive immunodiffusion (ID) against calf thymus extract, chemiluminescent immunoassay (CIA), and line blot immunoassay (LIA). RESULTS: Ninety-one patients with a mean disease duration of 2.36 years were included. Anti-Scl-70 antibodies by ID predicted a faster rate of FVC% decline (b = -0.06, P = 0.04). None of the other clinical or serological variables significantly predicted ILD progression. Interestingly, anti-Scl-70 antibodies as determined by CIA and LIA were not significant predictors of FVC decline (P = 0.26 and 0.64, respectively). The observed level of agreement between ID and LIA was moderate (κ = 0.568), whereas it was good between ID and CIA (κ = 0.66). CONCLUSION: Anti-Scl-70 antibodies determined by ID predicted faster FVC decline in patients with SSc-related ILD. Notably, both CIA and LIA for the same antibody did not predict rate of FVC decline at their current cutoffs of positivity. The discrepancy observed between anti-Scl-70 antibody assays can have relevant implications for clinical care and trial enrichment strategies in SSc-ILD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".