Fear of the Unknown: Can We Help Individuals With a Systemic Autoimmune Rheumatic Disease Deal With Uncertainty?
Bibliographic record
Abstract
Unfortunately, not much is certain in systemic autoimmune rheumatic diseases (SARDs). People with a SARD such as systemic lupus erythematosus (SLE) and systemic sclerosis (SSc) are dealing with a chronic, inflammatory, and often unpredictable autoimmune condition that might cause them to experience illness-related uncertainty.1,2 In chronic disease, illness-related uncertainty may occur because of ambiguity concerning the state of the illness and the complexity regarding treatment and the healthcare system; the disease course and prognosis may also be unpredictable.3 Further, uncertainty may occur because there is a lack of information about the diagnosis or because available information is probabilistic in nature. As Wallace and colleagues highlight in this issue of The Journal of Rheumatology , individuals with a SARD may experience considerable illness-related uncertainty, especially when the cause of their rheumatic disease or disease progression is unknown, when symptoms are unpredictable and changeable, or when there is a lack of knowledge about treatment options and outcomes.4 Patients with chronic disease may further experience uncertainty around the potential effect of their disease on social roles and opportunities, as well as on their ability to work, to perform daily activities, and to pursue favorite pastimes and hobbies.5 For individuals with SARD, uncertainty around treatment for their disease may arise from a number of additional factors. There is often scientific uncertainty around the risks and benefits of treatments for SARDs1 that in turn may influence treatment decisions made by clinicians and patients alike. The fact that it is often unclear how different treatments compare also contributes to the uncertainty experienced by patients and clinicians. Further, the probabilistic nature … Address correspondence to Dr. G. Simons, Rheumatology Research Group, Institute of Inflammation and Ageing (IIA), Queen Elizabeth Hospital, University of Birmingham, Birmingham, B15 2WB, UK. Email: g.simons{at}bham.ac.uk.
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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 | 0.015 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.014 | 0.026 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".