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Record W7115029673

Healthcare Use Patterns for High Volume Musculoskeletal Shoulder Disorders: A Longitudinal Cohort from the US Military Health System

2025· article· en· W7115029673 on OpenAlexaff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2025
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsInstitute of Population and Public Health
Fundersnot available
KeywordsHealth careCohortBiostatisticsOrthopedic surgeryMedical diagnosisEpidemiologyMusculoskeletal disorderRetrospective cohort studyAcromioclavicular joint
DOInot available

Abstract

fetched live from OpenAlex

Steven Z George,1 Sarah Morton-Oswald,2 Hui-Jie Lee,2 Maggie E Horn,3 Nrupen A Bhavsar,4 Daniel I Rhon5 1Departments of Orthopaedic Surgery and Population Health Sciences, Duke Clinical Research Institute, Duke University, Durham, NC, USA; 2Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA; 3Departments of Orthopaedic Surgery and Population Health Sciences, Duke University, Durham, NC, USA; 4Departments of Surgery and Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA; 5Department of Physical Medicine and Rehabilitation, F. Edward Hébert School of Medicine, Uniformed Services University, Bethesda, MD, USACorrespondence: Steven Z George, Email steven.george@duke.eduBackground: Shoulder injuries are a prevalent form of musculoskeletal disorders and common reason to seek healthcare. Health system level care utilization patterns for shoulder disorders are unknown. Accordingly, we described the frequency and timing of diagnostic imaging and treatment for a new episode of shoulder pain and determine usage variations across common diagnostic subgroups, military, and private sector care clinics.Methods: A retrospective cohort of US Military Health System beneficiaries (n = 456,241) classified into 1) non-specific shoulder diagnosis only, 2) rotator cuff/sub-acromial pain, 3) acromioclavicular (AC joint) dysfunction, 4) shoulder instability/dislocation, 5) hypomobility/adhesive capsulitis, 6) osteoarthrosis, and 7) multiple diagnoses. Outcomes were healthcare use encounters within the first three months of the index visit classified into diagnostic imaging, pharmacological, and non-pharmacological treatments.Results: The mean age of the cohort was 41 years old (SD 13). A majority of the cohort never received diagnostic imaging (76.7%). Advanced imaging was common for the multiple diagnoses group (53.6% of all advanced imaging). NSAIDS was the most common pharmacological treatment with 10.4% receiving at least one prescription, and physical therapy was the most common nonpharmacologic treatment received by 31% of the cohort. There was lower physical therapy and active treatment use and higher MRI or X-ray use for the same diagnostic group when care was initiated in a civilian clinic. Patients with rotator cuff disorders, multiple shoulder diagnoses, and hypomobility disorders were likely to have received at least one steroid joint injection if care was initiated in civilian compared to military clinics (28.1% vs 16%; 41.2% vs 32%; and 18.6% vs 13.3%, respectively).Conclusion: Care patterns for high volume shoulder injuries were largely congruent across military and civilian clinics. However, for specific diagnostic groups, use of imaging, steroid injections and physical therapy varied notably between military and civilian clinics.Keywords: shoulder condition, health care utilization, shoulder disorder, care patterns

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)0.002
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 score0.035
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.163
GPT teacher head0.535
Teacher spread0.373 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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