Living with a systemic autoinflammatory disease: burden of disease and effects on quality of life—an international patient survey
Bibliographic record
Abstract
Objectives: Systemic autoinflammatory diseases (SAIDs) significantly impact patients' and their families' quality of life (QoL). This international study aimed to evaluate the extent of this burden by gathering patient-reported data on their SAIDs and its effects on daily living and well-being. Methods: A 24-question online survey, developed by the Autoinflammatory Alliance and KAISZ/VAISZ in English and Dutch, was distributed internationally from 2017 to 2018. The survey included both closed-ended and open-ended questions addressing the SAID diagnosis and perceived impact on QoL. Participants were recruited through online social media platforms, and responses were collected using convenience sampling. Results: A total of 371 responses were received. Most respondents were from the United States (64%). The most common diagnoses were undifferentiated SAIDs (24%); periodic fever, aphthous stomatitis, pharyngitis, adenitis (19%); and cryopyrin-associated periodic syndrome (12%). Participants saw an average of 6.5 doctors before diagnosis, with 7% seeing over 20 doctors. Common symptoms included headaches (94%), fatigue (86%), pain (80%), and fever (79%). Assessment of QoL revealed an average score of 61 of the 100 between flares, which dropped to 18 of the 100 during flares. Daily activities were severely limited during flares for 52.5% of participants; 81% of participants reported that SAIDs impacted their work, career, or education. Conclusions: This study highlights the profound impact of SAIDs on QoL, emphasising the need for ongoing research and improved care for these rare conditions. Early diagnosis, targeted treatment, and psychosocial support can help mitigate the burden of these diseases.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".