Recurrent Anterior Shoulder Instability Treated Using the Arthroscopic Bankart–Latarjet Technique: Experience of a Peripheral Hospital
Bibliographic record
Abstract
The arthroscopic Bankart-Latarjet procedure is used in the surgical management of anterior shoulder instability. This technique is mainly performed in referral centers due to its high technicity. This study aimed to evaluate surgical outcomes in a peripheral hospital center. This is a retrospective study of patients treated for recurrent anterior shoulder instability. The clinical scores (Walch-Duplay, Rowe, and Western Ontario Shoulder Instability Index (WOSI)) were assessed preoperatively and at 12 months after surgery. The consolidation and the position of the bone block were evaluated at 6 months using a CT scan. Between 2016 and 2020, 40 patients had been operated on (mean age: 28.5 ± 7.9 years). During a mean follow-up of 29.5 ± 11.6 months, we noted only one complication, a case of fracture of the callus of a consolidated bone block. No recurrence of instability was recorded. The Walch-Duplay score increased from 17.8 to 94.6, the Rowe score from 24.9 to 96.8, and the WOSI score decreased from 52.1% to 6.9%. The bone block was consolidated in 35 patients (87.5%), and a flush position with the anterior edge of the glenoid was noted for all patients. At one year, 67.0% of the patients practicing sport had returned to sports. The arthroscopic Bankart-Latarjet technique was a reliable procedure in the hands of an experienced shoulder surgeon, even in a peripheral hospital center.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".