Abnormal Nailfold Capillaroscopy Is Common in Patients with Connective Tissue Disease and Associated with Abnormal Pulmonary Function Tests
Bibliographic record
Abstract
OBJECTIVE: To assess the presence of a systemic sclerosis (SSc) pattern on nailfold capillary microscopy (NCM) in patients with Raynaud phenomenon (RP) and to explore its association with abnormal pulmonary function tests (PFT). METHODS: NCM patterns were assessed in 759 consecutive patients with RP. Patterns were classified as normal (n = 354), nonspecific (n = 159), or SSc pattern (n = 246). Abnormal PFT was defined as forced vital or diffusion capacity < 70%. Patients were classified as primary RP (n = 245), or secondary: no definite diagnosis (n = 391), SSc (n = 40), primary Sjögren syndrome (pSS; n = 30), systemic lupus erythematosus (SLE; n = 30), mixed connective tissue disease (MCTD; n = 7), rheumatoid arthritis (RA; n = 15). RESULTS: An SSc pattern on NCM was frequently observed in most patients with a definite diagnosis: SSc (88%), pSS (33%), SLE (17%), MCTD (71%), and RA (13%). In patients without definite diagnosis, 17% had a normal NCM pattern, 35% nonspecific, and 48% SSc pattern. Abnormal PFT was more frequent in patients with an SSc pattern (35.9% vs 19.5%, p = 0.002), even when corrected for SSc diagnosis (p = 0.003). Absence of an SSc pattern had high negative predictive value (88%); positive predictive values were low. CONCLUSION: SSc pattern on NCM is common in patients with RP, and in those with connective tissue diseases other than SSc. It is associated with a higher prevalence of abnormal PFT, independent of the presence of an SSc diagnosis. Although these data need validation in a prospective setting, they underline the importance of NCM in RP and putative value to stratify the risk of pulmonary involvement in early stages of disease.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".