Use of Portable Ultrasound Machine for Outpatient Orthopedic Diagnosis: An Implementation Study
Bibliographic record
Abstract
INTRODUCTION: Ultrasonography and magnetic resonance imaging (MRI) are used to evaluate shoulder disorders. This implementation study investigated outpatient ultrasonography at an orthopedic practice in a managed care setting. METHODS: A portable ultrasound machine was introduced at an orthopedic clinic in a group practice. An orthopedic surgeon who primarily treated shoulder disorders received 15 hours of training. The impact of physician-performed ultrasonography on subsequent MRI and other outcomes of patients with shoulder disorders from January 2011 through October 2011 was determined using automated administrative and clinical data. Comparisons were made to patients who did not undergo ultrasonography at the experimental practice and 2 orthopedic clinics in the same practice. RESULTS: During the study, 146 ultrasound examinations were administered. Compared with patients who did not undergo ultrasonography, patients who received ultrasonography had significantly higher comorbidity. However, they were significantly less likely to have MRI (9.7% with ultrasonography vs 14.4% without; p = 0.03) although equally likely to undergo surgery (33.6% with ultrasonography vs 22.1% without, p = 0.77). Mean time to surgery was 89.3 ± 49.3 days for patients with ultrasonography vs 32.9 ± 43.3 days for patients without (p < 0.05). No ultrasonography-examined patients had an incorrect diagnosis at surgery. For patients receiving ultrasonography, an estimated 35 MRIs were avoided, saving a predicted $17,603, a 50% return in less than 1 year on a $34,897 investment for an ultrasound machine and supplies. CONCLUSION: Outpatient ultrasonography by an orthopedic surgeon can be useful for diagnosing shoulder disorders and might reduce MRI utilization.
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 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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".