Imaging Utilization Patterns in the Follow-Up of Extremity Soft Tissue Sarcomas in the United States
Bibliographic record
Abstract
This study aimed to describe patterns of imaging utilization after resection of extremity soft tissue sarcoma in the United States, assess for potential disparities, and evaluate temporal trends. A retrospective cohort study using a national database of private payer claims data was performed to determine the utilization rate of extremity and chest imaging in a 5-year postoperative follow-up period for patients with extremity soft tissue sarcoma treated between 2007 and 2019. Imaging utilization was assessed according to patient demographics (age, sex, race and ethnicity, and region of residency), calendar year of surgery, and postoperative year. Associations of demographic variables with imaging use were assessed using chi-square tests, trends in imaging use were analyzed using the Cochran-Armitage trend test or linear regression, and associations of postoperative year with imaging use were evaluated with the Pearson Correlation coefficient. A total of 3707 patients were included. Most patients received at least 1 chest (74%) and extremity (53%) imaging examination during their follow-up period. The presence of surveillance imaging was significantly associated with age (P < 0.0001) and region (P = 0.0029). Over the study period, there was an increase in use of extremity MRI (P < 0.05) and ultrasound (P < 0.01) and chest CT (P < 0.0001) and a decrease in use of chest radiographs (P < 0.0001). Imaging use declined over postoperative years (decrease by 85%-92% from year 1-5). In conclusion, the use of surveillance imaging varied according to patient demographics and has increased for extremity MRI and ultrasound and chest CT over the study period.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".