P130 Unmet needs and treatment preferences concerning digital ulcers in patients with systemic sclerosis
Bibliographic record
Abstract
Abstract Background/Aims Digital Ulcers (DU) affect around half of systemic sclerosis (SSc) patients and are associated with significant pain and difficulties in daily life. Despite available treatment options, DUs are often recalcitrant and recurrent. Our aim was to examine the patients’ perspectives concerning the unmet needs and treatment of SSc-DUs. Methods SSc patients with past DU were invited through international patient associations/social media to participate in an online English-language survey. The survey was launched on 31st January 2023 and open for four weeks. Results A total of 358 evaluable responses were collected from 11 countries, mainly from USA (65.6%), UK (11.5%) and Canada (4.5%). 81,6% of respondents were aged 30-70 years and 93% were female. Almost all (96.1%) had >1DU during the course of their disease (46% >10 DUs), mainly localised on the fingertips (84.9%). DUs have broad-ranging impacts: activities of daily living (79% ‘agreed’ or ‘strongly agreed’), work activities (73% ‘agreed’ or ‘strongly agreed’), future planning (64% ‘agreed’ or ‘strongly agreed’), and interpersonal relationships and/or social activities (59% ‘agreed’ or ‘strongly agreed’). Only one quarter (26.2%) of respondents were satisfied with currently available treatments, or treatment efficacy on main ulcer symptoms such as pain (24%). Half (51.7%) of respondents received wound/ulcer care, with only a third (31.4%) via a dedicated rheumatological/wound care clinic. The most frequent DU interventions were: wound cleaning (58.9%), ulcer dressing (63.2%) and debridement (27%), while botulinum (8.1%), fat injection (1.6%), sympathectomy (10.3%), and surgery (16.8%) were less frequent. Among respondents, the majority (71.3%) were ‘likely’ or ‘very likely’ to consider local DU treatment, 68.4% oral therapy, 43.8% intravenous treatments and 30.4% surgical approach. Figure 1 presents respondents perceived factors that may delay DU healing (1A), reasons to seek healthcare professional advice (1B), and reasons to change treatment (1C). Education about tDU complications is limited (34.1%), including recognition (30.8%), and actions to be taken (25%). Conclusion DUs have significant broad-ranging impacts there are many unmet needs have emerged. Local wound care is not standardized across specialist centers and patient education is often neglected. Dedicated treatment recommendations are urgently needed to optimise the therapeutic strategy, including non-pharmacological interventions. Disclosure G. Bandini: None. A. Alunno: None. B. Ruaro: None. I. Galetti: None. B. Alcacer-Pitarch: None. F. Oliveira Pinheiro: None. G. Campanaro: None. J. Jade: None. L. Muir: None. A. Moggi Pignone: None. Z. McMahan: None. K. El Aoufy: None. M. Matucci Cerinic: None. M. Hughes: None.
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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".