Dactylitis: A hallmark of psoriatic arthritis
Bibliographic record
Abstract
OBJECTIVE: Dactylitis-long considered a hallmark clinical feature of psoriatic arthritis (PsA)-occurs in 16-49% of patients with PsA. In this review, we discuss the pathology of dactylitis in PsA and clinical and imaging tools used to diagnose and monitor dactylitis. METHODS: PubMed literature searches were conducted using the terms psoriatic arthritis, dactylitis, pathology, imaging, ultrasound, magnetic resonance imaging, clinical, and indices. Articles were deemed relevant if they provided insight into the pathology, diagnosis, and/or monitoring of dactylitis in PsA, or if they discussed clinical or imaging indices used to assess dactylitis. RESULTS: Dactylitis in PsA often occurs asymmetrically, involves the feet more than the hands, and affects multiple digits simultaneously. Although dactylitis can be assessed clinically, imaging (radiography, ultrasound, magnetic resonance imaging, and bone scintigraphy) has provided key insights by documenting the various anatomic targets affected. Although inflammation can occur in most of the digital compartments, the nail has not been as well studied in dactylitic digits. Outcome measures for dactylitis range from dichotomous documentation to the Leeds dactylometer. Imaging outcome tools utilizing magnetic resonance imaging or ultrasound are under development. CONCLUSION: Dactylitis, which is associated with more erosive forms of PsA, is often the inaugural feature of PsA and may be the only feature for months to years. Early diagnosis and treatment of PsA favors better outcomes, possibly mitigating radiographic progression and destructive changes. Ultrasound and magnetic resonance imaging are useful tools that have not only shed light on the diverse tissues affected in dactylitis but can also be used to document ongoing inflammation. Ultrasound imaging dactylitis scores are being developed that will assist in diagnosing and documenting which compartments optimally respond to various treatment modalities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".