Impact of Radiographic Imaging of the Shoulder Joint on Patient Management: An Advanced-Practice Physical Therapist's Approach
Bibliographic record
Abstract
Purpose: Recent care innovations using advanced-practice physical therapists (APPs) as alternative health care providers are promising. However, information related to the clinical decision making of APPs is limited with respect to ordering shoulder-imaging investigations and the impact of these investigations on patient management. The purpose of this study was twofold: (1) to explore the clinical decision making of the APP providing care in a shoulder clinic by examining the relationship between clinical examination findings and reasons for ordering imaging investigations and (2) to examine the impact on patient management of ordered investigations such as plain radiographs, ultrasound (US), magnetic resonance imaging (MRI), and magnetic resonance arthrogram (MRA). Method: This was a prospective study of consecutive patients with shoulder complaints. Results: A total of 300 patients were seen over a period of 12 months. Plain radiographs were ordered for 241 patients (80%); 39 (13%) received MRI, 27 (9%) US, and 7 (2%) MRA. There was a relationship between clinical examination findings and ordering plain radiographs and US (ps=0.047 to <0.0001). Plain radiographs ordered to examine the biomechanics of the glenohumeral joint affected management (χ 2 1 =8.66, p=0.003). Finding a new diagnosis was strongly correlated with change in management for all imaging investigations (ps=0.001 to <0.0001). Conclusion: Skilled, extended-role physical therapists rely on history and clinical examination without overusing costly imaging. The most important indicator of change in management was finding a new diagnosis, regardless of the type of investigation ordered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".