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 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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".