Late Nailfold Videocapillaroscopy Pattern Associated With Hand Calcinosis and Acro‐Osteolysis in Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: To determine whether calcinosis and acro-osteolysis are related to specific nailfold videocapillaroscopy (NVC) features in patients with systemic sclerosis (SSc; scleroderma). METHODS: NVC and bilateral hand radiographs were systematically performed in 155 consecutively recruited patients with SSc during a 24-month period. Radiologic assessment of calcinosis and acro-osteolysis was performed blinded for the results on NVC features. RESULTS: Patients with calcinosis (n = 29) or acro-osteolysis (n = 25) on radiographs were more likely to have the late pattern on NVC, defined by severe loss of capillaries and neoangiogenesis (P = 0.003 and P < 0.001, respectively). A reduced number of capillaries was significantly found in patients with calcinosis (mean ± SD 3.55 ± 1.76 versus 5.53 ± 2.32 capillaries per finger; P < 0.001) and acro-osteolysis (mean ± SD 2.88 ± 1.30 versus 5.60 ± 2.26 capillaries per finger; P < 0.001). In addition, neoangiogenesis was more frequently observed in patients with severe acro-osteolysis (P = 0.021). Multivariate logistic regression analysis confirmed the independent association between the late NVC pattern and calcinosis (odds ratio [OR] 3.04, 95% confidence interval [95% CI] 1.20-7.68) or acro-osteolysis (OR 4.57, 95% CI 1.66-12.55), together with history of and/or active digital ulcers. CONCLUSION: Acro-osteolysis and calcinosis are independently associated with the late NVC pattern and particularly with severe capillary loss. These results strengthen the link between these radiographic lesions and digital destructive vasculopathy. Moreover, severe acro-osteolysis was more likely to occur with neoangiogenesis, which may suggest an attempt to compensate bone resorption. Further studies are now needed to better understand the physiopathology of calcinosis and acro-osteolysis and determine whether any agent may modify the course of these lesions by influencing vessel damages.
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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".