The Multidimensional Symptom Index: A new patient‐reported outcome for pain phenotyping, prognosis and treatment decisions
Bibliographic record
Abstract
BACKGROUND: There are few patient-reported outcomes routinely used that capture frequency and interference of different pain-related symptoms on a single scale. The purpose of this study was to describe the development and initial validation of the new Multidimensional Symptom Index (MSI). METHODS: Items were generated from patient interviews of the experience of chronic pain. Health valuations were created from rankings of 82 healthy subjects for each of 120 symptom (×10) × frequency (×3) × interference (×4) combinations using preference-based health valuations (0-100). Ranks for each symptom combination were then used in scale scoring. A sample of 300 patients with acute or chronic pain subsequently completed the MSI and a battery of other tools. Exploratory (EFA) and Confirmatory (CFA) factor analyses were triangulated with theory to arrive at the factor structure. Convergent validity was tested against established measures. RESULTS: Health rankings resulted in scores of 0-12 for each of the 10 symptom types. Factor analyses revealed two factors: MSI Somatic Symptoms and MSI Non-Somatic Symptoms. The MSI also quantified number of symptoms experienced (/10), mean frequency (/3) and mean interference (/4). The indices showed appropriate associations with the established PROs. CONCLUSIONS: The MSI is a new symptom-focused PRO that allows patient phenotyping and may have value for screening, prognosis and evaluating change. SIGNIFICANCE: This article presents the development and psychometric properties of a new measure of pain and related symptom frequency and interference. This measure could aid clinicians in establishing clinically relevant pain phenotypes for screening, prognosis and treatment decisions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".