Puncturing Plaques
Bibliographic record
Abstract
PURPOSE: To test and validate magnetic resonance imaging (MRI) sequences for peripheral artery lesion characterization and relate the MRI characteristics to the amount of force required for a guidewire to puncture peripheral chronic total occlusions (CTOs) as a surrogate for immediate failure of endovascular therapy. METHODS: voxels) with T2-weighted (T2W) and ultrashort echo time (UTE) sequences on a 7-T MR scanner. The MR images (n=15) were validated with micro-computed tomography and histology. CTOs (n=40) were classified by their MR signal characteristics as "soft" (signals indicating fat, thrombus, microchannels, or loose fibrous tissue), "hard" (collagen and/or speckled calcium signals), or "calcified" (calcified nodule signals). A 2-kg load cell advanced the back end of a 0.035-inch stiff guidewire at a fixed displacement rate (0.05 mm/s) through the CTOs, and the forces required to cross each lesion were measured. RESULTS: T2W images showed fat as hyperintense and hardened tissue as hypointense. Calcium and thrombus appeared as a signal void in conventional MRI sequences but were easily identified in UTE images (thrombus was hyperintense and calcium hypointense). MRI accurately differentiated "hard," "soft," and "calcified" CTOs based on associated guidewire puncture force. The guidewire could not enter "calcified" CTOs (n=6) at all. "Hard" CTOs (n=9) required a significantly higher (p<0.001) puncture force of 1.71±0.51 N vs 0.43±0.36 N for "soft" CTOs (n=25). CONCLUSION: MRI characteristics of PAD lesions correlate with guidewire puncture forces, an important aspect of crossability. Future work will determine if clinical MR scanners can be used to predict success in peripheral vascular interventions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".