Medically explained symptoms: a mixed methods study of diagnostic, symptom and support experiences of patients with lupus and related systemic autoimmune diseases
Bibliographic record
Abstract
OBJECTIVES: The aim was to explore patient experiences and views of their symptoms, delays in diagnosis, misdiagnoses and medical support, to identify common experiences, preferences and unmet needs. METHODS: Following a review of LUPUS UK's online forum, a questionnaire was posted online during December 2018. This was an exploratory mixed methods study, with qualitative data analysed thematically and combined with descriptive and statistically analysed quantitative data. RESULTS: There were 233 eligible respondents. The mean time to diagnosis from first experiencing symptoms was 6 years 11 months. Seventy-six per cent reported at least one misdiagnosis for symptoms subsequently attributed to their systemic autoimmune rheumatic disease. Mental health/non-organic misdiagnoses constituted 47% of reported misdiagnoses and were indicated to have reduced trust in physicians and to have changed future health-care-seeking behaviour. Perceptions of physician knowledge and listening skills were highly correlated with patient ratings of trust. The symptom burden was high. Fatigue had the greatest impact on activities of daily living, yet the majority reported receiving no support or poor support in managing it. Assessing and treating patients holistically and with empathy was strongly felt to increase diagnostic accuracy and improve medical relationships. CONCLUSION: Patient responses indicated that timely diagnosis could be facilitated if physicians had greater knowledge of lupus/related systemic autoimmune diseases and were more amenable to listening to and believing patient reports of their symptoms. Patient priorities included physicians viewing them holistically, with more emotional support and assistance in improving quality of life, especially in relation to fatigue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".