Validity and reliability of the Persian version of the STarT musculoskeletal tool
Bibliographic record
Abstract
Background The Subgrouping for Targeted Treatment (STarT) musculoskeletal (MSK) tool stratifies patients with MSK disorders (MSDs) into prognostic categories based on poor outcomes.Purpose This study aimed at investigating the validity and reliability of the Persian STarT MSK tool in people suffering from painful MSDs in Iran.Methods A total of 593 subjects with painful MSDs including neck, shoulder, low back, knee, and multisite pain received and completed the STarT MSK tool, visual analog scale (VAS), EuroQol five-dimensions three-levels questionnaire (EQ-5D-3 L), short form-36 health survey questionnaire (SF-36), and Örebro musculoskeletal pain screening questionnaire (ÖMPSQ) in the first visit. To examine test–retest reliability, 234 patients completed the STarT MSK tool 2 days after the initial visit.Results In this study, 139 (23.5%), 266 (44.9%), and 188 (31.7%) participants were classified as low-, medium-, and high-risk groupings for poor outcomes, respectively. Spearman’s correlation coefficient showed a strong relationship among Persian STarT MSK tool and EQ-5D-3 L (−0.78), SF-36 (−0.76), and OMPSQ (0.70). The results of known-group validity indicated that this tool could distinguish among the participants in different risk subgroups based on the scores of the ÖMPSQ, VAS, SF36, and EQ-5D-5 L (p < .001). No ceiling and floor effects were observed. Cronbach’s alpha and intra-class correlation coefficient (ICC2,1) were acceptable (0.71) and excellent (0.98), respectively.Conclusion The Persian version of STarT MSK tool has shown to be a valid and reliable instrument to stratify people with painful MSDs into low-, medium-, and high-risk subgroups based on persistent pain disability.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".