Patients’ unmet needs and treatment preferences concerning digital ulcers in systemic sclerosis
Bibliographic record
Abstract
Objective Digital ulcers (DUs) significantly impact on quality of life and function in patients with systemic sclerosis (SSc). The aim of our survey was to explore patients’ perspectives and their unmet needs concerning SSc-DUs. Methods SSc patients were invited through international patient associations and social media to participate in an online survey. Results A total of 358 responses were obtained from 34 countries: US (65.6%), UK (11.5%) and Canada (4.5%). Recurrent DUs were found to be common: >10 DUs (46.1%), 5–10 DUs (21.5%), 1–5 DUs (28.5%), 1 DU (3.9%). Fingertip DUs were most frequent (84.9%), followed by those overlying the IP joints (50.8%). The impact of DUs on patients is considerable, from broad-ranging emotional impacts to impact on activities of daily living, and on personal relationships. Around half of the respondents (51.7%) reported that they received wound/ulcer care, most often provided by non-specialist wound care clinics (63.8%). There was significant variation in local (wound) DU care, in particular regarding the use of debridement and pain management. DU-related education was only provided to one-third of patients. One-quarter of the patients (24.6%) were ‘very satisfied’ or ‘satisfied’ that the provided DU treatment(s) relieved their DU symptoms. Pain, limited hand function, and ulcer duration/chronicity were the main reasons for patients to consider changing DU treatment. Conclusion Our data show that there is a large variation in DU treatment between countries. Patient access to specialist wound-care services is limited, and only a small proportion of patients had their DU needs met. Moreover, patient education is often neglected. Evidence-based treatment pathways are urgently needed for DU management.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".