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 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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".