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Record W2822043946 · doi:10.1177/0363546518781331

The Incidence of Glenohumeral Bone and Cartilage Lesions at the Time of Anterior Shoulder Stabilization Surgery: A Comparison of Patients Undergoing Primary and Revision Surgery

2018· article· en· W2822043946 on OpenAlexaboutno aff
Kyle R. Duchman, Carolyn M. Hettrich, Natalie Glass, Robert W. Westermann, Brian R. Wolf, Keith M. Baumgarten, Julie Y. Bishop, Jonathan T. Bravman, Robert H. Brophy, James E. Carpenter, Grant L. Jones, John E. Kuhn, C. Benjamin, Robert G. Marx, Eric C. McCarty, Bruce S. Miller, McCarty Eric, Rick W. Wright, Alan L. Zhang

Bibliographic record

VenueThe American Journal of Sports Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsnot available
FundersNational Center for Advancing Translational Sciences
KeywordsMedicineSurgeryCartilageIncidence (geometry)Shoulder surgeryAnatomy

Abstract

fetched live from OpenAlex

BACKGROUND: Intra-articular glenohumeral joint changes frequently occur after shoulder instability events. PURPOSE: (1) To compare demographic characteristics, baseline patient-reported outcomes, and intraoperative findings for patients undergoing primary or revision shoulder stabilization surgery and (2) to determine the incidence of glenohumeral bone and cartilage lesions in this population while identifying factors independently associated with these lesions. STUDY DESIGN: Cross-sectional study; Level of evidence, 3. METHODS: The Multicenter Orthopaedic Outcomes Network (MOON) Shoulder Group shoulder instability database was used to identify all prospectively enrolled patients undergoing shoulder stabilization surgery for anterior instability between October 2012 and September 2016. Any patient who underwent surgery for posterior or multidirectional shoulder instability or concomitant rotator cuff repair surgery was excluded. Patient demographic characteristics, preoperative patient-reported outcomes, and intraoperative findings, including glenohumeral bone and cartilage lesions, were compared for patients undergoing primary and revision shoulder stabilization surgery. Additionally, patients with and without glenohumeral bone and cartilage lesions were compared and independent associations determined using multivariate analysis. RESULTS: There were 545 patients available for analysis (461/545 [84.6%] primary; 84/545 [15.4%] revision). Patients undergoing revision surgery were older ( P = .001), were more frequently smokers ( P = .022), had a greater number of instability events before surgery ( P = .047), more frequently required reduction assistance ( P < .001), and had lower Short Form-36 (SF-36) Mental Component Summary ( P = .020) and Western Ontario Shoulder Instability Index (WOSI) ( P = .026) scores preoperatively. Additionally, patients undergoing revision surgery had a higher frequency of bone and cartilage lesions than those undergoing primary surgery (47.6% vs 18.4%, respectively; P < .001). Male sex, revision surgery, black race, increasing body mass index, increasing patient age, and lower preoperative SF-36 Physical Component Summary score were independently associated with the presence of glenohumeral bone and cartilage lesions at the time of shoulder stabilization surgery. Revision surgery was strongly associated with the presence of glenohumeral bone and cartilage lesions (odds ratio [OR], 4.381 [95% CI, 2.591-7.406]) and glenoid bone loss greater than 10% (OR, 9.643 [95% CI, 5.128-18.134]) or 20% (OR, 13.076 [95% CI, 5.113-33.438]) of the glenoid width. CONCLUSION: Glenohumeral bone and cartilage lesions are common at the time of shoulder stabilization surgery, occurring more frequently in patients undergoing revision surgery as compared with primary surgery. On the basis of these findings, future prospective studies should aim to compare the clinical outcomes in these 2 groups.

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 score0.002
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
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.308
Teacher spread0.286 · 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

Citations39
Published2018
Admission routes1
Has abstractyes

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