Reliability, Discriminative, and Prognostic Validity of the Multidimensional Symptom Index in Musculoskeletal Trauma
Bibliographic record
Abstract
OBJECTIVES: The Multidimensional Symptom Index (MSI) is a 10-item parallel score frequency×interference patient-reported outcome for use in clinical pain research. This manuscript describes results related to measurement stability, discriminative accuracy when screening for major depressive disorder (MDD), and prognostic validity when predicting recovery trajectories after acute musculoskeletal (MSK) trauma. METHODS: Data were drawn from a longitudinal cohort study of adults with acute MSK trauma, supplemented by a secondary sample of adults with chronic pain. RESULTS: In a sample of n=23 stable participants over a 1-month period, reliability metrics indicated good stability for all 5 subscales (ICC3,1: 0.70 to 0.91). In a mixed acute/chronic sample (n=148), the Number of Symptoms and Nonsomatic Symptoms subscales showed clinically useful discriminative accuracy for MDD screening (area under the curve=0.86 and 0.88, respectively). In n=129 with acute MSK trauma, the Mean Interference and Nonsomatic Symptoms subscales showed significant prognostic validity for classifying participants into "recovery expected" or "recovery not expected" groups with 72.5% and 92.2% accuracy, respectively. DISCUSSION: The MSI holds promise as a tool for evaluating change, screening for MDD, and identifying those at high or low risk of poor recovery. The results favor sensitivity over specificity. The labile nature of the acute pain symptoms and a truncated distribution of Nonsomatic Symptoms scores in that group both require some caution in interpretation. The MSI appears to be a potentially useful tool for rapid pain phenotyping, evaluation, and quick screening purposes in clinical practice.
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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 | 0.010 | 0.026 |
| 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".