Evaluation of the Use of Artificial X-Rays for Educational and Intraoperative Guidance During C-Arm Positioning
Bibliographic record
Abstract
Fluoroscopic C-arms are operated by medical radiography technologists (RTs) in the Canadian operating room (OR). While they do receive formal, accredited training, most of this training is theoretical, rather than hands-on. During their first encounters in the OR, new RTs can experience difficulty achieving the radiographic views required by surgeons, often needing several scout X-rays during C-arm positioning before achieving the correct anatomical view. Furthermore, ambiguous language by surgeons often inadequately conveys their request (Pally 2013). The result is often frustration, unnecessary radiation exposure, and added OR time (Booij 2007). Several groups have tried to address this problem by overlaying artificial X-ray images on a live video feed (Chen 2013, Reaungamornat 2012, Müller 2011). Others have used artificial X-rays for simulation training (Bott 2008, Gong 2014, Cleary 2004). Though the intent is to improve C-arm positioning accuracy and efficiency during orthopaedic procedures, these systems have primarily been evaluated on system accuracy and not on their potential to decrease radiation exposure inside the OR or C-arm positioning time. The purpose of this study was therefore to evaluate the value of artificial X-rays in enhancing C-arm positioning performance using inexperienced users.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| 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".