Modified Weaver-Dunn Technique Using Transosseous Bone Tunnels and Coracoid Suture Augmentation
Bibliographic record
Abstract
INTRODUCTION: A modified Weaver-Dunn procedure for the management of acromioclavicular joint injuries that uses transosseous bone tunnels and coracoid suture augmentation is described with associated clinical results. METHODS: A retrospective review of 39 consecutive patients who underwent a primary mWD procedure by a single surgeon from January 2013 to July 2019 was conducted. Patient charts and radiographs were reviewed for clinical course, complications and management, and radiographic evaluation. Satisfaction, American Shoulder and Elbow Surgeons (ASES), Single Assessment Numeric Evaluation, and Simple Shoulder Test scores were obtained. RESULTS: A total of 28 patients (72%) with a mean follow-up of 37.5 (12 to 84 months) and a mean age of 44.3 ± 15.1 years were included. Postoperative ASES, Simple Shoulder Test, Single Assessment Numeric Evaluation, and satisfaction scores were 90.6 ± 14.2, 11.1 ± 1.5, 87.3 ± 10.2, and 4.4 ± 1.2 (out of 5), respectively, with a significant improvement in ASES of 42.2 ± 21.8 points (P < 0.001). All patients had significant decrease in coracoclavicular distance (P < 0.001). Three patients (10.7%) had complications, with two (7.1%) requiring additional surgery. CONCLUSION: Excellent functional and radiographic outcomes can be achieved with this modified Weaver-Dunn technique. Complication and revision rates are comparable with those that are found in the literature. LEVEL OF EVIDENCE: Level IV, Retrospective cohort study.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".