Use of Standardized Language for C-arm Fluoroscopy Improves Intraoperative Communication and Efficiency
Bibliographic record
Abstract
INTRODUCTION: Intraoperative fluoroscopy is a ubiquitous tool in orthopaedic surgery. However, many orthopaedic surgeons and radiology technologists are not taught standard terminology to communicate with one another. Breakdown of communication leads to inefficiencies. Simulation studies have demonstrated that a common language for C-arm movements may reduce time to capture the desired images and number of radiographs required. Our objective was to investigate the effect of a standardized language protocol for intraoperative C-arm fluoroscopy on communication as perceived by the surgeon and radiology technologists. METHODS: Our study intervention was the implementation of a common C-arm fluoroscopy terminology education protocol. To evaluate the efficacy of this protocol, a survey was administered to orthopaedic surgeons and radiology technologists after procedures involving the use of intraoperative fluoroscopy. Study end points were measured using a 5-point Likert scale and included effectiveness of communication, need for obtaining repeat radiographs, need to correct the C-arm position, and confusion noted during surgery. This survey was administered before and after the study intervention. RESULTS: The study intervention resulted in a statistically significant improvement in the mean perceived quality of intraoperative communication between the surgeon and the radiology technologist (0.398 [0.072, 0.725], P = 0.017). There was also a reported decrease in confusion in the operating room (-0.572 [-0.880, -0.263], P < 0.001), movement correction of the C-arm fluoroscope (-0.592 [-0.936, -0.248], P = 0.001), and need for repeat radiographs (-0.782 [-1.158, -0.406], P < 0.001) after the implementation of a standardized fluoroscopy language. CONCLUSION: A standardized fluoroscopy language protocol improves intraoperative communication between orthopaedic surgeons and radiology technologists.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".