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Record W2794380659 · doi:10.1177/0363546518755752

Descriptive Epidemiology of the MOON Shoulder Instability Cohort

2018· article· en· W2794380659 on OpenAlexaboutno aff
Matthew J. Kraeutler, Eric C. McCarty, John W. Belk, Brian R. Wolf, Carolyn M. Hettrich, Shannon F. Ortiz, Jonathan T. Bravman, Keith M. Baumgarten, Julie Y. Bishop, Matthew Bollier, Robert H. Brophy, James L. Carey, James E. Carpenter, Brian T. Feeley, John A. Grant, Grant L. Jones, John E. Kuhn, John D. Kelly, C. Benjamin, Robert G. Marx, Bruce S. Miller, Brian J. Sennett, Matthew V. Smith, 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
Fundersnot available
KeywordsEpidemiologyCohortInstabilityMedicineDemographyGeographyPhysicsSociologyInternal medicineMechanics

Abstract

fetched live from OpenAlex

BACKGROUND: Shoulder instability is a common diagnosis among patients undergoing shoulder surgery. PURPOSE: To perform a descriptive analysis of patients undergoing surgery for shoulder instability through a large multicenter consortium. STUDY DESIGN: Case series; Level of evidence, 4. METHODS: All patients undergoing surgery for shoulder instability who were enrolled in the MOON Shoulder Instability Study were included. Baseline demographics included age, sex, body mass index, and race. Baseline patient-reported outcomes (PROs) included the American Shoulder and Elbow Surgeons (ASES) score, Shoulder Activity Score, Western Ontario Shoulder Instability Index (WOSI), 36-Item Health Survey (RAND-36), and Single Assessment Numeric Evaluation (SANE). The preoperative physician examination included active range of motion (ROM) and strength testing. Preoperative imaging assessments with plain radiography, magnetic resonance imaging (MRI), and computed tomography were also included and analyzed. RESULTS: Twenty-six surgeons had enrolled 863 patients (709 male, 154 female) across 10 clinical sites. The mean age for the cohort was 24 years (range, 12-63 years). Male patients represented 82% of the cohort. The primary direction of instability was anterior for both male (74%) and female (73%) patients. Football (24%) and basketball (13%) were the most common sports in which the primary shoulder injury occurred. No clinically significant differences were found in preoperative ROM between the affected and unaffected sides for any measurement taken. Preoperative MRI scans were obtained in 798 patients (92%). An anterior labral tear was the most common injury found on preoperative MRI, seen in 66% of patients, followed by a Hill-Sachs lesion in 41%. Poor PRO scores were recorded preoperatively (mean: ASES, 72.4; WOSI, 43.3; SANE, 46.6). CONCLUSION: The MOON Shoulder Instability Study has enrolled the largest cohort of patients undergoing shoulder stabilization to date. Anterior instability is most common among shoulder instability patients, and most patients undergoing shoulder stabilization are in their early 20s or younger. The results of this study provide important epidemiological information for patients undergoing shoulder stabilization surgery.

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)0.004
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
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 head0.046
GPT teacher head0.351
Teacher spread0.304 · 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

Citations114
Published2018
Admission routes1
Has abstractyes

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