The Symptom Burden Index: Development and Initial Findings from Use with Patients with Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: Our study had 3 aims: (1) to evaluate the functioning of the Symptom Burden Index (SBI) in patients with systemic sclerosis (SSc); (2) to determine the amount of burden per problem experienced by patients as well as the number of patients experiencing each measured problem area, and the number of SSc problems per patient; and (3) to characterize the burden profiles of problem area-specific subgroups of patients. METHODS: We developed the SBI to determine the effect of problems in 8 major symptomatic areas of importance to patients (skin, hand mobility, calcinosis, shortness of breath, eating, bowel, sleep, and pain). RESULTS: Sixty-two patients with SSc completed questionnaires on current disease-related problems, physical functioning, and health status. On average, patients were 53.4 years old and had had SSc for 8 years. Patients were mainly women (87%), English-speaking (87%), with diffuse SSc (63%), white (69%), married (61%), and lived with 1 or more additional household members (84%). Only 26% were employed full-time. The 3 most widely reported problem areas were pain, hand, and skin, experienced by 92%, 89%, and 88%, respectively. About one-third reported experiencing 0-5 problems and one-third 7-8 problems; individual patients experienced, on average, 5.7 problems. CONCLUSION: Psychometric evaluation determined that (1) summarizing SBI problem area item sets to report burden scores per problem measured is justified; (2) the 8 proposed problem areas are independent and deserve separate evaluation; and (3) burden scores correlate as expected with the Health Assessment Questionnaire-Disability Index and the Medical Outcomes Study Short-Form 36 questionnaire. The number of problems experienced and the degree of problem-associated burden that patients with SSc bear are substantial. Use of the SBI's patient-focused measurements may aid physicians in resolving problems most directly affecting patients' quality of life. This approach to measuring symptomatic burden in patients with chronic disease could be extended to other conditions.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".