Patient Experience of Sjögren’s Disease and its Multifaceted Impact on Patients’ Lives
Bibliographic record
Abstract
INTRODUCTION: The symptoms associated with Sjögren's disease (Sjögren's) are well-documented from the physician's perspective. However, from the patient's perspective, there is limited information on symptoms and their impact on health-related quality of life (HRQoL). This study aimed to provide an expanded understanding of patients' experience of Sjögren's and how symptoms impact HRQoL using a novel multi-method social media listening (SML) approach. METHODS: A total of 26,950 social media posts with relevant content on Sjögren's posted by social media users from the USA, Canada, Australia, UK, France, Germany, Italy, Spain and China were analysed using an artificial intelligence natural language processing tool to explore patient conversations. Symptoms by level of impact on patients were characterised based on 'commonness' and 'bothersomeness'. Applied concept association analysis was used to assess relationships between symptom domains and impact domains. A qualitative framework was applied to explore words and phrases patients use to describe symptoms and their impacts. RESULTS: Five of the identified symptom domains were very impactful: Pain; Dry Mouth and Throat; Fatigue, Energy and Sleep; Emotional Balance; and Dry Eye. The symptom domains Pain and Dry Mouth and Throat were the most common, while those of Emotional Balance and Fatigue, Energy and Sleep were the most bothersome. Symptom domains most closely associated with four HRQoL impact domains were Fatigue, Energy and Sleep, Dry Mouth and Throat and Dry Eye with Daily Functioning; Fatigue, Energy and Sleep with Financial Health; Emotional Balance with Psychological Wellbeing and Gynaecological Issues with Social Wellbeing. CONCLUSION: The results of this SML study show that Sjögren's affects diverse aspects of patients' lives, with symptoms extending beyond dry eyes and mouth and impacting daily living and functioning. Because symptoms may affect patients differently, these results highlight the importance of measuring impact on HRQoL to assess patient outcomes and treatment options in routine clinical practice and clinical trials.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".