Reporting items for capillaroscopy in clinical research on musculoskeletal diseases: a systematic review and international Delphi consensus
Bibliographic record
Abstract
OBJECTIVES: The level of detail included when describing nailfold videocapillaroscopy (NVC) methods varies among research studies, making interpretation and comparison of results challenging. The overarching objective of the present study was to seek consensus on the reporting standards in NVC methodology for clinical research in rheumatic diseases and to propose a pragmatic reporting checklist. METHODS: Based on the items derived from a systematic review focused on this topic, a three-step web-based Delphi consensus on minimum reporting standards in NVC was performed among members of the European League against Rheumatism (EULAR) Study Group on Microcirculation in Rheumatic Diseases and the Scleroderma Clinical Trials Consortium. RESULTS: A total of 319 articles were selected by the systematic review, and 46 items were proposed in the Delphi process. This Delphi exercise was completed by 80 participants from 31 countries, including Australia and countries within Asia, Europe, North America and South America. Agreement was reached on items covering three main areas: patient preparation before NVC (15 items), device description (5 items) and examination details (13 items). CONCLUSION: Based on the available evidence, the description of NVC methods was highly heterogeneous in the identified studies and differed markedly on several items. A reporting checklist of 33 items, based on practical suggestions made (using a Delphi process) by international participants, has been developed to provide guidance to improve and standardize the NVC methodology to be applied in future clinical research studies.
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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 | 0.689 | 0.670 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.019 |
| Bibliometrics | 0.025 | 0.018 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.009 | 0.019 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".