The Short, 5‐Item Shoulder Instability–Return to Sport After Injury Score Performs as Well as the Longer Version in Predicting Psychological Readiness to Return to Sport
Bibliographic record
Abstract
PURPOSE: To reduce the length of the Shoulder Instability-Return to Sport After Injury (SIRSI) scale and determine the predictive validity of the short version compared with the original form. METHODS: This study included patients who underwent an arthroscopic Bankart repair or open Latarjet procedure between 2017 and 2019. One group was used for the SIRSI scale-reduction process, and a second group was used to test the predictive validity of the proposed short SIRSI scale. The Cronbach α value was used to evaluate internal consistency. Validity was determined by calculating the Pearson correlation coefficient with the Western Ontario Shoulder Instability Index scale. Predictive validity was assessed using receiver operating characteristic (ROC) curve statistics. RESULTS: A total of 158 patients participated in the scale-reduction process, and 137 patients participated in the predictive-validation process. The SIRSI scale was successfully reduced to a 5-item scale constructed by 1 underlying factor accounting for 60% of the variance. The short version showed good internal consistency (Cronbach α = 0.82) and was highly correlated with the Western Ontario Shoulder Instability Index scale and the long version. The short SIRSI scores were significantly different between patients who returned to sports and those who did not. The SIRSI scale had excellent predictive ability for return-to-sport outcomes (area under ROC curve of 0.84 for short version [95% confidence interval, 0.7-0.9] and 0.83 for long version [95% confidence interval, 0.7-0.9]). CONCLUSIONS: A valid 5-item, short version of the SIRSI scale was successfully developed in our patient population. The short version was found to be as robust as the long scale for discriminating and predicting return-to-sport outcomes. LEVEL OF EVIDENCE: Level II, prospective cohort study.
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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 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".