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Record W4381982982 · doi:10.1016/j.asmr.2023.03.014

Patients Aged >50 Years With Anterior Shoulder Instability Have a Decreased Risk of Recurrent Dislocation After Operative Treatment Compared With Non‐Operative Treatment

2023· article· en· W4381982982 on OpenAlexfundno aff
Anne A. Smartt, Ryan R. Wilbur, Bryant M. Song, Aaron J. Krych, Kelechi R. Okoroha, Jonathan D. Barlow, Christopher L. Camp

Bibliographic record

VenueArthroscopy Sports Medicine and Rehabilitation · 2023
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsnot available
FundersInstitute of AgingNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institute on AgingMayo ClinicStrykerArthrexMusculoskeletal Transplant FoundationArthritis Foundation
KeywordsMedicineDislocationInstabilitySurgeryAnterior shoulder dislocationAnterior shoulderMaterials science

Abstract

fetched live from OpenAlex

Purpose To compare the clinical outcomes of operative and nonoperative management, identify risk factors for recurrent instability, and identify risk factors for progression to surgery after failed nonoperative management for patients with first‐time anterior shoulder dislocation after the age of 50 years. Methods An established geographic medical record system was used to identify patients who experienced a first‐time anterior shoulder dislocation after the age of 50 years. Patient medical records were reviewed to identify treatment decisions and outcomes of interest, including rates of frozen shoulder and nerve palsy, progression to osteoarthritis, recurrent instability, and progression to surgery. Outcomes were evaluated using Chi‐square tests and survivorship curves were generated using Kaplan‐Meier methods. A Cox model was developed to evaluate for potential risk factors of recurrent instability and progression to surgery after an initial trail of at least 3 months of nonoperative treatment. Results 179 patients were included with a mean follow‐up of 11 years. 14% ( n = 26) underwent early surgery within 3 months and 86% ( n = 153) were initially treated nonoperatively. Mean age (59 years), was similar for both groups, but those that underwent early surgery had an increased rate of full‐thickness rotator cuff tears (82% vs 55%; P = .01), labral tears (24% vs 8.0%; P = .01), and humeral head fracture (23% vs 8.5%; P = .03). When comparing the early surgery group to the nonoperative group, there were similar rates of persistent moderate‐severe pain (19% vs 17%; P = .78) and frozen shoulder (8 vs 9%, respectively; P = .87) at final follow‐up. Although nerve palsy (19% vs 8%; P = .08) and progression to osteoarthritis (20% vs 14%; P = .40) were more common in surgical patients, they experienced lower rates of recurrent instability after surgical intervention (0% vs 15%; P = .03) compared to nonoperatively treated patients. Increasing number of instability events prior to presentation was the greatest risk factor for recurrent instability (HR 232; P < .01). Fourteen percent ( n = 21) failed initial nonoperative treatment and proceeded to surgical intervention at an average of 4.6 years after the initial instability event, and the greatest risk factors for progression to surgery were recurrent instability (HR 3.41; P < .01). Conclusions Although the majority of patients >50 years that experience ASI are treated nonoperatively, those that require surgery tend to have more significant injury pathology, a lower risk of recurrent instability after surgery, but a higher progression to osteoarthritis compared to patients that do not require surgical intervention. There was no difference in pain severity at final follow‐up, rates of frozen shoulder or nerve palsy between patients who underwent initial nonoperative treatment after instability and those who underwent surgery. A history of multiple instability episodes prior to presentation was the greatest predictor of recurrent instability and failure of nonoperative treatment and progression to surgery. Level of Evidence Level III, 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)0.000
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 score0.036
Threshold uncertainty score0.915

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 studies0.0000.001
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 head0.014
GPT teacher head0.333
Teacher spread0.319 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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