Subjective Shoulder Value for Sport Is a Simple, Reliable, and Valid Score to Assess Shoulder Function in Athletes
Bibliographic record
Abstract
PURPOSE: To validate the subjective shoulder value for sport (SSV-Sport) by measuring its correlation with existing patient-reported outcome measures (PROMs) and defining its psychometric properties. METHODS: Between May 2021 and May 2022, we established 2 patient groups. Group 1 included those (1) aged 18 years or older, who were (2) consulting for the first time for any shoulder condition, (3) regularly participated in sports, and were capable of accessing a questionnaire independently. There were asked to rate their SSV and SSV-Sport at admission and 2 weeks later; they also were asked to answer a questionnaire including other PROMS. Group 2 comprised patients who had (1) undergone shoulder stabilization surgery and had (2) a minimum follow-up period of 6 months. RESULTS: For the shoulder disability patients (group 1, n = 62), there was a strong and significant correlation between SSV-Sport and other PROMs: Quick Disabilities of the Arm, Shoulder and Hand Sport (r = 0.84), Walch-Duplay (r = 0.65), Rowe (r = 0.74), Western Ontario Shoulder Instability (r = 0.78), and SSV (r = 0.75) (P = .0001). The SSV-Sport was reliable at baseline and 2 weeks after (0.91, 95% confidence interval 0.85-0.94), and was responsive to change (P < .001). For the anterior instability patients (group 2, n = 83), SSV was on average 50 points greater than SSV-Sport (29.2 vs 79.4, P < .001) for preoperative values. In both groups, the values of SSV were constantly and significantly higher than the values of SSV-Sport (81.9 ± 21.3 vs 54.8 ± 30.9; P < .001). CONCLUSIONS: The SSV-Sport is an easily administered, reliable, responsive, and valid measure of shoulder function in athletes that is highly correlated with other PROMs. SSV-Sport is better adapted than SSV to quantify pre- and postoperative shoulder deficiency in athletes. LEVEL OF EVIDENCE: Level III, cohort study (diagnosis).
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".