Development and Initial Validation of the Satisfaction and Recovery Index (SRI) for Measurement of Recovery from Musculoskeletal Trauma
Bibliographic record
Abstract
BACKGROUND: There is a need for a generic patient-reported outcome (PRO) that is patient-centric and offers sound properties for measuring the process and state of recovery from musculoskeletal trauma. This study describes the construction and initial validation of a new tool for this purpose. METHODS: A prototype tool was constructed through input of academic and clinical experts and patient representatives. After evaluation of individual items, a 9-item Satisfaction and Recovery Index (SRI) was subject to psychometric evaluation drawn from classical test theory. Subjects were recruited through online and clinical populations, from those reporting pain or disability from musculoskeletal trauma. The full sample (N = 129) completed the prototype tool and a corresponding region-specific disability measure. A subsample (N = 46) also completed the Short-Form 12 version 2 (SF12vs). Of that, a second subsample (N = 29) repeated all measures 3 months later. RESULTS: A single factor 'health-related satisfaction' was extracted that explained 71.1% of scale variance, Cronbach's alpha = 0.95. A priori hypotheses for cross-sectional correlations with region-specific disability measures and the generic Short-form 12 component scores were supported. The SRI tool was equally responsive to change, and able to discriminate between recovered/non-recovered subjects, at a level similar to that of the region-specific measures and generally better than the SF-12 subscales. CONCLUSION: The new SRI tool, as a measure of health-related satisfaction, shows promise in this initial evaluation of its properties. It is generic, patient-centered, and shows overall measurement properties similar to that of region-specific measures while allowing the potential benefit of comparison between clinical conditions. Despite early promising results, additional properties need to be explored before the tool can be endorsed for routine clinical use.
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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 | 0.017 | 0.028 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".