Performance of Forced Vital Capacity and Lung Diffusion Cutpoints for Associated Radiographic Interstitial Lung Disease in Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: Forced vital capacity (FVC) and DLCO are used for screening of systemic sclerosis-associated interstitial lung disease (SSc-ILD). The study purpose was to determine the sensitivity, specificity, and negative predictive value (NPV) (proportion of true negative screening tests) of FVC and DLCO thresholds for SSc-ILD on chest high-resolution computed tomography (HRCT) scans. METHODS: Patients fulfilling American College of Rheumatology 2013 SSc criteria with a chest HRCT scan and pulmonary function tests (PFT) were studied. A thoracic radiologist quantified radiographic ILD. Optimal FVC and DLCO % predicted thresholds for ILD were identified using receiver-operating characteristic curves. The FVC and DLCO combinations with greatest sensitivity and specificity were also determined. Subanalysis was performed in patients with positive Scl-70 autoantibodies. RESULTS: The study included 265 patients. Of 188 (71%) with radiographic ILD, 59 (31%) had "normal" FVC (≥ 80% predicted), and 65 out of 151 (43%) had "normal" DLCO (≥ 60% predicted). FVC < 80% (sensitivity 0.69, specificity 0.73), and DLCO < 62% (sensitivity 0.60, specificity 0.70) were optimal thresholds for radiographic SSc-ILD. All FVC and DLCO threshold combinations evaluated had NPV < 0.70. The NPV for radiographic ILD for FVC < 80% was lower in patients with positive Scl-70 autoantibody (NPV = 0.05) compared to negative Scl-70 autoantibody (NPV = 0.57). CONCLUSION: Radiographic ILD is prevalent in SSc despite "normal" PFT. No % predicted FVC or DLCO threshold combinations yielded high NPV for SSc-ILD screening. "Normal" FVC and DLCO in patients with SSc, especially those with positive Scl-70 autoantibodies, should not obviate consideration of HRCT for ILD evaluation.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".