Health Perceptions and HRQL With Soft‐Tissue Sarcoma at 12 Months Post‐Op: Using the Wilson‐Cleary Model to Evaluate the Measurement Properties of the RNLI and EQ‐5D‐3L
Bibliographic record
Abstract
INTRODUCTION: It is increasingly apparent that the most used patient-reported outcome measure in health-related quality of life (HRQL) soft-tissue sarcoma research (Toronto Extremity Salvage Score) is limited by its exclusive focus on physical function. It is now recommended that it only be used in combination with other global outcome measures, such as the Reintegration to Normal Living Index (RNLI) and Euroqol-5D-3L (EQ-5D-3L). We assessed the measurement properties of the RNLI and EQ-5D using the Wilson-Cleary Model and sought to better understand health perceptions and HRQL at 12 months post-op. METHODS: Data for this secondary analysis were drawn from an inception cohort of people receiving care for soft-tissue sarcoma at our institution. Inclusion criteria were being ≥ 18 years old and a diagnosis of localized soft-tissue sarcoma (biopsy-confirmed). Measures included the MSTS-87 (pain), RNLI (health perceptions), and EQ-5D-3L (HRQL). RStudio was used to calculate descriptive statistics, assess internal consistency, and evaluate the measurement and structural models. RESULTS: The study sample (n = 276) was 45% female with a mean age of 56 (18). Internal consistency was high with the RNLI (α = 0.91) and acceptable with EQ-5D-3L (α = 0.74). Findings suggested good model fit with the measurement model (CFI = 0.98, RMSEA = 0.37, SRMR = 0.0) and structural model (CFI = 0.98, RMSEA = 0.37, SRMR = 0.08). Moreover, HRQL appeared most impacted by the ability to engage in daily activities (work/study, home maintenance, family affairs, and leisure). CONCLUSION: The RNLI (health perceptions) and EQ-5D (HRQL) appeared to be reliable and valid with this patient group. Findings suggest targets for optimizing soft-tissue sarcoma outcomes are maximizing functional restoration, encouraging participation in fulfilling activities throughout recovery (even if adapted), and routine psychosocial distress monitoring.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".