Nailfold capillaroscopy by smartphone-dermatoscope for connective tissue disease diagnosis in interstitial lung disease: a prospective observational study
Bibliographic record
Abstract
Nailfold capillaroscopy (NFC) is a non-invasive tool validated for systemic sclerosis diagnosis. The role and interpretation of NFC in interstitial lung disease (ILD) patients for the diagnosis of connective tissue disease associated ILD (CTD-ILD) remains undefined. In a prospective study, quantitative and qualitative NFC by smartphone-dermatoscope (3M Dermlite-DL4ΤΜ attached to iPhone-6plusΤΜ) was performed in 96 patients with well-defined CTD-ILD (n=27) and non-CTD ILD (n=69; idiopathic interstitial pneumonia n=42, interstitial pneumonia with autoimmune features n=27) by ILD-multidisciplinary meeting. NFC scoring was performed by two independent, blinded specialist rheumatologists. Comprehensive baseline clinical, serological, physiological and radiological data were included. Multivariable models for CTD diagnosis in ILD, comprising nailfold characteristics at empirical thresholds determined by receiver operating characteristic curve analysis and clinical variables, were explored. In 94 patients with complete NFC data (total 687 images, median eight images per patient from eight digits), low capillary density (<6 capillaries/millimetre), increased giant capillaries (≥3), avascular areas (≥2) and microhaemorrhages all strongly enhanced the discrimination of CTD-ILD from non-CTD ILD (OR 5.00-7.47) independent of clinical covariates. In multivariable analysis, low capillary density and microhaemorrhages were independent predictors of CTD in ILD additional to the risk conferred by serology and radiology. Microhaemorrhages were also a strong predictor of CTD (adjusted OR 13.45, p=0.006) independent of clinical manifestations. All pre-specified qualitative NFC classification schemes identified CTD-ILD (OR range 3.27-8.47). NFC performed by smartphone-dermatoscope is an accessible, clinically feasible tool that may improve the identification of CTD further to routine clinical assessment of the ILD patient.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".