Learning from discordance in patient and physician global assessments of systemic lupus erythematosus disease activity.
Bibliographic record
Abstract
OBJECTIVE: Differences have been described between patient and physician assessments of well being in several chronic illnesses, and these differences may affect outcome. Disagreement may lead to dissatisfaction and to behaviors with dangerous consequences. We describe and identify predictors of patient-physician differences on ratings of disease activity in systemic lupus erythematosus (SLE). METHODS: Data collected on 154 patients included age, education, disease duration, and patient and physician global assessments of lupus activity on a 10 cm visual analog scale (VAS), the Health Assessment Questionnaire (HAQ), the Medical Outcome Study Short-Form 36 (SF-36), the Systemic Lupus Disease Activity Index (SLEDAI), the Systemic Lupus Activity Measure (SLAM-R), and the Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (SDI). Multiple linear regression models were performed using patient VAS scores, physician VAS scores, and patient minus physician VAS scores as the dependent variables, and age, disease duration, selected SF-36 and SLAM-R subscales, and SDI as independent variables. RESULTS: Patients were 90% female and 80% Caucasian, with a mean education of 13 +/- 2.8 years and a mean age of 43.1 +/- 13.6 years. The overall mean disease duration was 10.5 +/- 7.8 years. Physicians overscored patients by 2.5 cm in 6% of the cases and patients overscored physicians in 16% of the cases. The best multivariate model to predict overall differences included SF-36 mental health and SLAM-R kidney scores. CONCLUSION: Patient-physician differences may result from a divergence in focus. Patients score lupus activity based on their psychological status, while physicians rely more heavily on the physical effect of the disease.
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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 | 0.012 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".