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Record W4252150697 · doi:10.1177/2325967119s00315

Management of The Failed Latarjet Procedure: Outcomes of Revision Surgery With Fresh Distal Tibial Allograft

2019· article· en· W4252150697 on OpenAlexaboutno aff
Andrew S. Bernhardson, Liam A. Peebles, Colin P. Murphy, Anthony Sanchez, Robert F. LaPrade, Matthew T. Provencher

Bibliographic record

VenueOrthopaedic Journal of Sports Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLatarjet procedureSurgeryCoracoidAnterior shoulderElbowPhysical examinationRadiography

Abstract

fetched live from OpenAlex

Objectives: A patient with recurrent instability after a failed Latarjet procedure remains a challenge to address. The vast majority of these result in large amounts of bone loss, resorption, and issues with retained hardware, and there is minimal literature that assesses outcomes of revision surgery following a failed Latarjet. The objective of this study was to determine the outcomes of patients who underwent revision surgery for a recurrent shoulder instability after a failed Latarjet procedure. Methods: All consecutive patients who presented with recurrent anterior shoulder instability after a Latarjet procedure were prospectively enrolled. Patients were included if they had a prior Latarjet, and a history and physical examination findings consistent with recurrent anterior shoulder instability. Patients were excluded if they had prior neurologic injury, a seizure disorder, bone graft requirements to the humeral head, or findings of multidirectional or posterior instability. History of shoulder instability was documented, including initial dislocation history, time of instability, number of prior procedures, and examination findings, as well as plain radiographic data and computed tomography (CT) scan obtained on all patients, and arthritis graded with Samilson and Prieto (SP) grade. All patients were treated with hardware removal, capsulo-labral release with subsequent repair and bony reconstruction via fresh distal tibial allograft to the glenoid. Outcomes pre- and post-revision were assessed with ASES (American Shoulder and Elbow Score), Single Assessment Numerical Evaluation (SANE), and Western Ontario Shoulder Index (WOSI), and statistically compared. All patients underwent a CT scan of the distal tibial allograft at a minimum time point 4 months after surgery. Results: There were 31 patients enrolled (all males), with mean age 25.5 (range, 19 to 38), and with a mean follow-up of 47 months (range, 36 to 60) after the revision with distal tibial allograft. All patients after their Latarjet presented with recurrent shoulder dislocation (11/31) or recurrent subluxation (20/31) and all patients had recurrent shoulder instability on examination. Radiographs demonstrated two fixation screws in all cases, mean SP grade of 0.5 (range, I to III), and CT scan demonstrated that mean 78% of the Latarjet coracoid graft had resorbed (range, 50% to 100%). Preoperative outcomes improved for ASES (40 to 92, p=0.001), SANE (44 to 91, p=0.001), and WOSI (1300 to 310, p=0.001). There were no recurrences, and a final CT scan of the distal tibia revision demonstrated a mean 92% of DTA remained, but 98% union at the glenoid-DTA interface. Conclusion: Although the failed Latarjet with subsequent instability remains a challenge, treatment with fresh a distal tibial allograft provided substantial improvement in terms of stability and function. The vast majority of the failed Latarjet procedures had near complete resorption of the coracoid graft and many had hardware complications. Additional long-term studies are necessary to determine the efficacy of this challenging revision population.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.264
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

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Citations2
Published2019
Admission routes1
Has abstractyes

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