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 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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".