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Record W4293149756 · doi:10.1177/03635465221118369

Relationship of the SIRSI Score to Return to Sports After Surgical Stabilization of Glenohumeral Instability

2022· article· en· W4293149756 on OpenAlexaboutno aff
Luciano Andrés Rossi, Ignacio Pasqualini, Rodrigo Brandariz, Nora Fuentes, Cecilia Fieiras, Ignacio Tanoira, Maximiliano Ranalletta

Bibliographic record

VenueThe American Journal of Sports Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineReceiver operating characteristicAthletesYouden's J statisticPhysical therapyReturn to sportLogistic regressionPredictive validityClinical psychologyInternal medicine

Abstract

fetched live from OpenAlex

Background: Literature is scarce regarding the influence of psychological readiness on return to sports after shoulder instability surgery. Purpose: To evaluate the predictive ability of the Shoulder Instability–Return to Sport after Injury (SIRSI) score in measuring the effect of psychological readiness on return to sports and to compare it between athletes who returned to sports and athletes who did not return to sports. Study Design: Cohort study; Level of evidence, 2. Methods: A prospective analysis was performed of patients who underwent an arthroscopic Bankart repair or a Latarjet procedure between January 2019 and September 2020. Psychological readiness to return to play was evaluated using the SIRSI instrument. Preoperative and postoperative functional outcomes were measured by the Rowe, Athletic Shoulder Outcome Scoring System, and Western Ontario Shoulder Instability Index scores. The predictive validity of the SIRSI was assessed by the use of receiver operating characteristic (ROC) curve statistics. The Youden index was calculated and used to determine a SIRSI score cutoff point that best discriminated psychological readiness to return to sports. A logistic regression analysis was performed to evaluate the effect of psychological readiness on return to sports and return to preinjury sports level. Results: A total of 104 patients were included in this study. Overall, 79% returned to sports. The SIRSI had excellent predictive ability for return-to-sport outcomes (return to sports: area under ROC curve, 0.87 [95% CI, 0.80-0.93]; return to preinjury sports level: area under ROC curve, 0.96; [95% CI, 0.8-0.9]). A cutoff level of ≥55 was used to determine whether an athlete was psychologically ready to return to sports and to return to preinjury sports level (Youden index, 0.7 and 0.9, respectively). Of those who returned to sports, 76.8% were psychologically ready to return to play, with a median SIRSI score of 65 (interquartile range, 57-80). In comparison, in the group that did not return to sports, only 4.5% achieved psychological readiness with a median SIRSI score of 38.5 (interquartile range, 35-41) ( P < .001). Regression analysis for the effect of SIRSI score on return to sports was performed. For every 10-point increase in the SIRSI score, the odds of returning to sports increased by 2.9 times. Moreover, those who did not achieve their preinjury sports level showed poorer psychological readiness to return to play and SIRSI score results. Conclusion: The SIRSI was a useful tool for predicting whether patients were psychologically ready to return to sports after glenohumeral stabilization surgery. Patients who returned to sports and those who returned to their preinjury sports level were significantly more psychologically ready than those who did not return. Therefore, we believe that the SIRSI score should be considered along with other criteria that are used to decide whether the patient is ready to return to sports.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.024
GPT teacher head0.303
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations46
Published2022
Admission routes1
Has abstractyes

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