Healthcare Use Patterns for High Volume Musculoskeletal Shoulder Disorders: A Longitudinal Cohort from the US Military Health System
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".