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Record W2748088522 · doi:10.1177/1758573217728414

Arthroscopic Bankart repair with remplissage for non-engaging Hill-Sachs lesion in professional collision athletes

2017· article· en· W2748088522 on OpenAlexaboutno aff
Peter Dömös, Francesco Ascione, Andrew L. Wallace

Bibliographic record

VenueShoulder & Elbow · 2017
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBankart repairBankart lesionAthletesExternal rotationSurgeryVisual analogue scalePhysical therapyArthroscopy

Abstract

fetched live from OpenAlex

Background The present study aimed to determine whether arthroscopic remplissage with Bankart repair is an effective treatment for improving outcomes for collision athletes with Bankart and non-engaging Hill-Sachs lesions. Methods Twenty collision athletes underwent arthroscopic Bankart repair with posterior capsulotenodesis (B&R group) and were evaluated retrospectively, using pre- and postoperative WOSI (Western Ontario Shoulder Instability), EQ-5D (EuroQOL five dimensions), EQ-VAS (EuroQol-visual analogue scale) scores and Subjective Shoulder Value (SSV). The recurrence and re-operation rates were compared to a matched group with isolated arthroscopic Bankart repair (B group). Results The mean age was 25 years with an mean follow-up of 26 months. All mean scores improved with SSV of 90%. There was a mean deficit in external rotation at the side of 10°. One patient was treated with hydrodilatation for frozen shoulder. One patient had residual posterior discomfort but no apprehension in the B&R group compared to 5% persistent apprehension in the B group. In comparison, the recurrence and re-operation rates were 5% and 30% ( p = 0.015), 5% and 35% ( p = 0.005) in the B&R and B groups, respectively. Conclusions This combined technique demonstrated good outcomes, with lower recurrence rates in high-risk collision athletes. The slight restriction in external rotation does not significantly affect any clinical outcomes and return to play.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.934

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.047
GPT teacher head0.372
Teacher spread0.325 · 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

Citations52
Published2017
Admission routes1
Has abstractyes

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