Use of the Patient-generated Index in Systemic Sclerosis to Assess Patient-centered Outcomes
Bibliographic record
Abstract
OBJECTIVE: To evaluate the content and construct validity of an individualized patient-reported instrument, the Patient-generated Index (PGI), in patients with systemic sclerosis (SSc), and to compare its performance to that of other instruments and to the Patient-reported Outcomes Measurement Information System (PROMIS) framework. METHODS: Patients identified the 5 most important life areas affected by SSc, which we categorized into domains of the PROMIS framework (mental, physical, and social). Correlations were obtained between PGI and the Health Assessment Questionnaire (HAQ), the Medical Outcomes Study Short Form-36 (SF-36), and the Symptom Burden Index (SBI) scores. RESULTS: Sixty-two patients with SSc completed the PGI: 87% women, 69% white, mean age 53 years, mean disease duration 8 years, and 63% with diffuse disease. A total of 258 individual life area responses were recorded: 54% in social health (social function and relationship subcomponents); 28% in physical health (physical function, symptoms, general physical health); and 19% in mental health (consisting largely of the affect subcomponent). Patient PGI responses were categorized into 6 of the 7 subcomponents of the PROMIS framework; substance use/alcohol was not identified. Statistically significant correlations ranging in absolute value from 0.26 to 0.50 were observed between the PGI and the HAQ, SF-36 summary component scores, and the large majority of SF-36 subscales and SBI components. CONCLUSION: The PGI is a personalized instrument that adequately assessed a wide range of health-related quality of life outcomes within the PROMIS framework. The PGI captured additional constructs not yet defined within the framework that are important for patients with SSc.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".