Neer Award 2019: Latarjet procedure vs. iliac crest bone graft transfer for treatment of anterior shoulder instability with glenoid bone loss: a prospective randomized trial
Bibliographic record
Abstract
BACKGROUND: The Latarjet and iliac crest bone graft transfer (ICBGT) procedures are competing treatment options for anterior shoulder instability with glenoid bone loss. METHODS: In this bicentric prospective randomized study, 60 patients with anterior shoulder instability and glenoid bone loss were included and randomized to either an open Latarjet or open ICBGT (J-bone graft) procedure. Clinical evaluation was completed before surgery and 6, 12, and 24 months after surgery, including the Western Ontario Shoulder Instability index, Rowe score, Subjective Shoulder Value, pain level, satisfaction level, and work and sports impairment, as well as assessment of instability, range of motion, and strength. Adverse events were prospectively recorded. Radiographic evaluation included preoperative, postoperative, and follow-up computed tomography analysis. RESULTS: None of the clinical scores showed a significant difference between the 2 groups (P > .05). Strength and range of motion showed no significant differences except for diminished internal rotation capacity in the Latarjet group at every follow-up time point (P < .05). A single postoperative traumatic subluxation event occurred in 2 ICBGT patients and 1 Latarjet patient. The type and severity of other adverse events were heterogeneous. Donor-site sensory disturbances were observed in 27% of the ICBGT patients. Computed tomography scans revealed a larger glenoid augmentation effect of the ICBGTs; this, however, was attenuated at follow-up. CONCLUSION: The Latarjet and ICBGT procedures for the treatment of anterior shoulder instability with glenoid bone loss showed no difference in clinical and radiologic outcomes except for significantly worse internal rotation capacity in the Latarjet group and frequently noted donor-site sensory disturbances in the ICBGT group.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".