Cross-cultural adaptation of the Thoracic Outlet Syndrome Index for Turkish-speaking patients with thoracic outlet syndrome
Bibliographic record
Abstract
Background/aim: Considering that there is no specifically designed scale to measure quality of life (QoL) and level of functioning among Turkish-speaking patients with thoracic outlet syndrome (TOS), the aim of this study was to adapt the Thoracic Outlet Syndrome Index (TOSI) to the Turkish language (TOSI-TR) and analyze its psychometric properties in patients with TOS. Materials and methods: Thirty-nine patients with symptomatic TOS were included in the study. The participants were assessed using the following measures: the QuickDASH, the Western Ontario Rotator Cuff Index (WORC), and a visual analog scale (VAS). The psychometric properties of the TOSI-TR were examined in terms of test-retest reliability, construct validity, convergent validity, and feasibility. Results: of the TOSI-TR was 0.949 (95% CI: 0.903-0.973). The Kaiser-Meyer-Olkin value was found to be 0.716 with a significant result for Bartlett's sphericity test (p < 0.001). The TOSI-TR had a one-factor solution explaining 74.05% of the total variance. There was a significant correlation between TOSI-TR scores and QuickDASH (r = 0.762, p < 0.001), WORC (r =0.794, p < 0.001), and VAS (r = 0.663, p < 0.001) scores. The WORC and VAS scores were significant determinants of the TOSI-TR score, explaining 65.3% of the variance. There were no floor or ceiling effects. Conclusion: The TOSI-TR is a reliable, valid, and feasible questionnaire for the QoL and functional status in Turkish-speaking patients with TOS. We recommend this 15-item scale for optimal assessment in patients with TOS.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".