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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".