Underdetection of Interstitial Lung Disease in Juvenile Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: Utilizing data obtained from a prospective, international, juvenile systemic sclerosis (SSc) cohort, the present study was undertaken to determine if pulmonary screening with forced vital capacity (FVC) and diffusing capacity for carbon monoxide (DLco) is sufficient to assess the presence of interstitial lung disease (ILD) in comparison to high-resolution computed tomography (HRCT) in juvenile SSc. METHODS: The juvenile SSc cohort database was queried for patients enrolled from January 2008 to January 2020 with recorded pulmonary function tests (PFTs) parameters and HRCT to determine the discriminatory properties of PFT parameters, FVC, and DLco in detecting ILD. RESULTS: Eighty-six juvenile SSc patients had both computed tomography imaging and FVC values for direct comparison. Using findings on HRCT as the standard measure of ILD presence, the sensitivity of FVC in detecting ILD in juvenile SSc was only 40%, the specificity was 77%, and area under the curve (AUC) was 0.58. Fifty-eight juvenile SSc patients had both CT imaging and DLco values for comparison. The sensitivity of DLco in detecting ILD was 76%, the specificity was 70%, and AUC was 0.73. CONCLUSION: The performance of PFTs in juvenile SSc to detect underlying ILD was quite limited. Specifically, the FVC, which is one of the main clinical parameters in adult SSc to detect and monitor ILD, would miss ~60% of children who had ILD changes on their accompanying HRCT. The DLco was more sensitive in detecting potential abnormalities on HRCT, but with less specificity than the FVC. These results support the use of HRCT in tandem with PFTs for the screening of ILD in juvenile SSc.
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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 | 0.003 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".