Innovations in imaging for chronic total occlusions: a glimpse into the future of angiography's blind-spot
Bibliographic record
Abstract
Chronic total occlusions (CTOs) are a subset of lesions that present a considerable burden to cardiovascular patients. There exists a strong clinical desire to improve non-surgical options for CTO revascularization. While several techniques, devices, and guide wires have been developed and refined for use in CTOs, the inability of angiography to adequately visualize occluded arterial segments makes interventions in this setting technically challenging. This review describes the current status of several invasive and non-invasive imaging techniques that may facilitate improved image guidance during CTO revascularization, with the goals of improving procedure safety and efficacy while reducing the time required to complete these interventions. Cardiac imaging also has important potential roles in selecting patients most likely to benefit from revascularization as well as pre-procedural planning, post-procedural assessment of revascularized segments and long-term outcomes studies. Modalities discussed include non-invasive techniques, such as CT(computed tomography) angiography and cardiac magnetic resonance imaging (MRI), as well as invasive techniques, such as intravascular ultrasound, optical coherence tomography, intravascular MRI, and conventional angiography. While some of these techniques have some evidence to support their use at present, others are at earlier stages of development. Strategies that combine imaging techniques with the use of interventional therapies may provide significant opportunities to improve results in CTO interventions and represent an active area of investigation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".