Diagnostic overshadowing in systemic lupus erythematosus (SLE): A qualitative study
Bibliographic record
Abstract
Objectives SLE diagnostic journeys can be protracted, with negative impacts on long-term health. This study explored the role of diagnostic overshadowing (DOS) in delaying SLE diagnoses. Methods A qualitative analysis of 268 completed SLE patient surveys and 25 in-depth interviews purposively selected from the 2018-2021 Cambridge University Systemic Autoimmune Rheumatic Disease (SARD) studies. Results The majority of participants appear to have experienced DOS and there were indications that sustained DOS (S-DOS) may add years to some SLE diagnostic journeys. Symptom misattributions which contributed to S-DOS included: (1) “ Medical mystery ”, particularly when the clinician indicated that it was too expensive to keep investigating. (2) Negative misattributions (e.g. “nothing seriously wrong”), often due to a failure to connect multiple symptoms as possible indicators of an underlying condition. (3 ) Diagnostic roadblocks, including, in the case of some participants, a mental health, psychosomatic, ME/CFS or fibromyalgia (mis)diagnosis. (4) Moral misattributions, such as to “malingering”, which could undermine patient help-seeking and/or clinician help-giving. Conclusion Our data suggests that DOS may be an important factor in diagnostic delay in patients with SLE.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".