Prevalence of Phantom Scanning in Cardiac Arrest and Trauma Resuscitations: The Scary Truth
Bibliographic record
Abstract
Background: The prevalence of phantom scanning, or point of care ultrasound (POCUS) performed without saving images, has not been well studied. Phantom scanning can negatively affect patient care, reduce billed revenue, and can increase medicolegal liability. We sought to quantify and compare the prevalence of phantom scanning among emergency department (ED) cardiac arrests and trauma resuscitations. Methods: This was a single center, retrospective cohort study from July 1, 2019, to July 1, 2021, of all occurrences of POCUS examination documented on the resuscitation run sheet during cardiac arrest and trauma resuscitations. Two investigators reviewed the run sheets to screen for POCUS documentation. Instances where documentation was present were matched with saved images in the picture archiving and communication system. Instances where documentation was present but no images could be located were considered phantom scans. A two-tailed student’s t test was utilized to compare the phantom scanning rate between cardiac arrest and trauma resuscitations. Results: A total of 1,862 patients were included in the study period, with 329 cardiac arrests and 401 trauma resuscitations having run sheet documentation of POCUS performance. The phantom scanning rate in cardiac arrests and trauma resuscitations was 70.5% (232/329) and 86.5% (347/401), respectively (p < 0.001). Conclusion: Phantom scanning is common in both cardiac arrests and trauma resuscitations in the ED at our institution, but is significantly higher in trauma resuscitations. Further research is needed to assess causes and develop potential solutions to reduce the high prevalence of phantom scanning.
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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 | 0.005 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| 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".