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Record W2321801605 · doi:10.1177/1758573214557147

Outcomes for intra-substance free coracoid graft in patients with antero-inferior instability and glenoid bone loss in a population of high-risk athletes at a minimum follow-up of 2 years

2014· article· en· W2321801605 on OpenAlexaff
Afshin Arianjam, Simon N. Bell, Jennifer Coghlan, Jason Old, Roger Sloan

Bibliographic record

VenueShoulder & Elbow · 2014
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineCoracoidShouldersBankart repairSurgeryElbowRetrospective cohort studyBone grafting

Abstract

fetched live from OpenAlex

BACKGROUND: The aim of this retrospective case series study was to assess the outcomes of patients with recurrent anterior shoulder instability with antero-inferior glenoid bone loss treated with a specific open stabilization technique using intra-substance coracoid bone-grafting and Bankart repair. METHODS: Over a 4-year period, 34 shoulders in all male patients of mean age 21 years were stabilized with this technique. Pre- and postoperative function, motion and stability were assessed as part of Rowe stability scoring, and American Shoulder and Elbow Surgeons (ASES) and Oxford Instability were recorded, with at least 2 years of follow-up in all patients. Union of the graft was determined by post-operative computed tomography (CT) of the affected shoulder. RESULTS: For all cases, two redislocations (5.9%) and two subluxations occurred when continuing high-risk sport after 2 years. Post-operative scores [median, mean (SD): Rowe 77.5, 77.2 (19.5); ASES 94.2, 92 (7.7); Oxford 43, 41.2 (6)]. CT scans on 28 shoulders at a mean of 4.5 months after surgery showed non-union in three cases (10%). CONCLUSIONS: These results demonstrate a high rate of success in cases of glenoid bone loss in the young contact athlete with recurrent instability treated with open stabilization and bone grafting.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.705

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.258
Teacher spread0.248 · 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 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".

Quick stats

Citations8
Published2014
Admission routes1
Has abstractyes

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