The arthroscopic treatment of anterior shoulder instability with glenoid bone loss shows similar clinical results after Latarjet procedure and iliac crest autograft transfer
Bibliographic record
Abstract
PURPOSE: Recurrent anterior shoulder instability caused by critical bone loss of the glenoid is a challenging condition for shoulder surgeons. The purpose of this prospective multicenter trial was to compare the arthroscopic transfer of the coracoid process (Latarjet procedure) with the arthroscopic reconstruction of the glenoid using iliac crest autografts. METHODS: A prospective multi-center trial was performed in nine orthopaedic centres in Austria, Germany and Switzerland between July 2015 and August 2021. Patients were prospectively enrolled and received either an arthroscopic Latarjet procedure or an arthroscopic iliac crest graft transfer. Standardized follow-up after 6 months and mimimum 24 months included range of motion, Western Ontario stability index (WOSI), Rowe score and subjective shoulder value (SSV). All complications were recorded. RESULTS: 177 patients (group Latarjet procedure: n = 110, group iliac crest graft: n = 67) were included in the study. WOSI (n.s.), SSV (n.s.) and Rowe score (n.s.) showed no difference at final follow-up. 10 complications were seen in group Latarjet procedure and 5 in group iliac crest graft; the frequency of complications did not differ between the two groups (n.s.). CONCLUSION: The arthrosopic Latarjet procedure and arthroscopic iliac crest graft transfer lead to comparable results regarding clinical scores, frequency of recurrent dislocations and complication rates. LEVEL OF EVIDENCE: Level II.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".