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Record W2786730954 · doi:10.1177/0363546517750628

Glenoid Bone Loss in Posterior Shoulder Instability: Prevalence and Outcomes in Arthroscopic Treatment

2018· article· en· W2786730954 on OpenAlexaboutno aff
Adam C. Hines, Jay B. Cook, James S. Shaha, Kevin P. Krul, Steve Shaha, John D. Johnson, Craig R. Bottoni, Douglas J. Rowles, John M. Tokish

Bibliographic record

VenueThe American Journal of Sports Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePosterior shoulderLabrumMagnetic resonance imagingArthroscopySurgeryRetrospective cohort studyAnterior shoulderRadiological weaponRadiology

Abstract

fetched live from OpenAlex

BACKGROUND: Glenoid bone loss is a well-accepted risk factor for failure after arthroscopic stabilization of anterior glenohumeral instability. Glenoid bone loss in posterior instability has been noted relative to its existence in posterior instability surgery. Its effect on outcomes after arthroscopic stabilization has not been specifically evaluated and reported. PURPOSE: The purpose was to evaluate the presence of posterior glenoid bone loss in a series of patients who had undergone arthroscopic isolated stabilization of the posterior labrum. Bone loss was then correlated to return-to-duty rates, complications, and validated patient-reported outcomes. STUDY DESIGN: Case-control study; Level of evidence, 3. METHODS: A retrospective review was conducted at a single military treatment facility over a 4-year period (2010-2013). Patients with primary posterior instability who underwent arthroscopic isolated posterior labral repair were included. Preoperative magnetic resonance imaging was used to calculate posterior glenoid bone loss using a standardized "perfect circle" technique. Demographics, return to duty, complications, and reoperations, as well as outcomes scores including the Single Assessment Numeric Evaluation and the Western Ontario Shoulder Instability Index (WOSI) scores, were obtained. Outcomes were analyzed across all patients based on percentage of posterior glenoid bone loss. Bone loss was then categorized as below or above the subcritical threshold of 13.5% to determine if bone loss effected outcomes similar to what has been shown in anterior instability. RESULTS: There were 43 consecutive patients with primary, isolated posterior instability, and 32 (74.4%) completed WOSI scoring. Mean follow-up was 53.7 months (range, 25-82 months) The mean posterior glenoid bone loss was 7.3% (0%-21.5%). Ten of 32 patients (31%) had no appreciable bone loss. Bone loss exceeded 13.5% in 7 of 32 patients (22%), and 2 patients (6%) exceeded 20% bone loss. Return to full duty or activity was nearly 90% overall. However, those with >13.5%, subcritical glenoid bone loss, were statistically less likely to return to full duty (relative risk = 1.8), but outcomes scores, complications, and revision rates were otherwise not different in those with no or minimal bone loss versus those with more significant amounts. CONCLUSION: Posterior glenoid bone loss has not previously been evaluated independently relative to patients with shoulder instability repairs. Sixty-nine percent of our patients had measurable bone loss, and 22% had greater than 13.5%, or above subcritical bone loss. While these patients were statistically less likely to return to full duty, the reoperation rate, complications, and patient-reported outcomes between groups were not different.

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)0.007
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.020
GPT teacher head0.331
Teacher spread0.312 · 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".

Quick stats

Citations60
Published2018
Admission routes1
Has abstractyes

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