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Record W4200003305 · doi:10.5435/jaaos-d-21-00732

Modified Weaver-Dunn Technique Using Transosseous Bone Tunnels and Coracoid Suture Augmentation

2021· article· en· W4200003305 on OpenAlexaff

Bibliographic record

VenueJournal of the American Academy of Orthopaedic Surgeons · 2021
Typearticle
Languageen
FieldMedicine
TopicShoulder and Clavicle Injuries
Canadian institutionsObject Research Systems (Canada)
Fundersnot available
KeywordsCoracoidFibrous jointRetrospective cohort studyCoracoid process

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.426
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.360
Teacher spread0.333 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American Academy of Orthopaedic SurgeonsSame topicShoulder and Clavicle InjuriesFrench-language works237,207