Harnessing the parallax for better spatial awareness
Bibliographic record
Abstract
Despite easy access to imaging diagnostic procedures and an abundance of spatial data, most cardiac interventions are still performed under two-dimensional fluoroscopy. Incorporating anatomical data from scans into procedures plans has the potential to improve the swiftness and outcomes of percutaneous cardiac interventions. Therefore, procedure planning based on the specific anatomy is becoming a new standard of excellence in interventional cardiology. Still, we often tend to disregard specific spatial relations and the actual direction of catheter tip movement inside the body, relying on a try and error approach. The precise spatial orientation of instruments and prosthetic devices is crucial, especially during structural heart interventions. Here, we present how deliberate movements of objects under fluoroscopy can reveal the spatial orientation of catheters and other devices. We also propose a novel "two-point rule" for identifying three-dimensional relations between points in space. Understanding and applying this rule might substantially increase the spatial awareness of operators performing cardiovascular interventions. Although the concept is pretty simple, using it "live" during interventional cardiology procedures requires thorough understanding and practice. We propose the "two-point rule" as a crucial rule to develop expertise in spatial orientation under fluoroscopy and ensure high-quality outcomes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".