Latarjet procedure enables 73% to return to play within 8 months depending on preoperative SIRSI and Rowe scores
Bibliographic record
Abstract
PURPOSE: Systematic reviews report return to play (RTP) within 5.8 months (range, 3-8) following the Latarjet procedure, but the factors that influence RTP remain unknown. The present study aimed to report the rate and time of return to play (RTP) during the first 8 months following the Latarjet procedure, and to determine the influence of sport type or patient characteristics. METHODS: The authors retrospectively collected the records of patients that underwent Latarjet procedures for anterior shoulder instability between 2015 and 2017. Patients were excluded if they had any concomitant rotator cuff tendon lesions, or previous ipsilateral shoulder surgery. The authors retrieved patient demographics, time from injury to surgery, type of sport practiced (overhead/non-overhead, contact/non-contact), as well as pre- and postoperative Western Ontario Shoulder Instability index (WOSI), Shoulder Instability-Return to Sport After Injury index (SIRSI), and Rowe score. RESULTS: A total of 217 patients (217 shoulders) were eligible for inclusion, comprising 184 males and 33 females, aged 26.8 ± 7.3 years at index surgery. The main sport practiced prior to surgery involved overhead (n = 173, 80%) and/or contact (n = 152, 70%) activities. By 8 month follow-up, 158 patients (73%) resumed their main sport, at a mean of 5.1 ± 1.5 months. Multivariable analysis revealed that RTP was more likely in patients with higher preoperative Rowe score (OR, 1.02; p = 0.024) and SIRSI score (OR, 1.02; p = 0.008). CONCLUSIONS: By 8 months following the Latarjet procedure, 73% of patients had resumed their main sport. The likelihood of RTP was significantly associated with preoperative Rowe and SIRSI scores, but not with sport type. LEVEL OF EVIDENCE: IV.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".