<i>In vitro</i> in‐stent restenoses evaluated by 3D ultrasound
Bibliographic record
Abstract
The purpose of this study was to quantify in-stent restenoses with 3D B mode and power Doppler ultrasound (U.S.) imaging. In-stent restenoses were mimicked with vascular phantoms in which a nonferromagnetic prototype stent (Boston Scientific) and a ferromagnetic clinical stainless steel stent (Palmaz P295) were embedded. Each phantom had an 80% in-stent stenosis and a 75% stenosis located outside the stent. These phantoms were compared to a reference phantom reproducing both stenoses without stent. Data sets of 2D cross-sectional U.S. images were acquired in freehand scanning using a magnetic sensor attached to the U.S. probe and in mechanical linear scanning with the probe attached to a step motor device. Each 2D image was automatically segmented before 3D reconstruction of the vessel. Results indicate that the reference phantom (without stent) was accurately assessed with errors below 1.8% for the 75% stenosis and 3.2% for the 80% stenosis in both B mode and power Doppler for the two scanning methods. The 80% in-stent stenoses in Boston Scientific and Palmaz stents were, respectively, evaluated at 73.8 (+/-3.2)% and 75.8 (+/- 3)% in B mode and at 82 (+/- 2.5)% and 86.2 (+/- 6.4)% in power Doppler when freehand scans were used. For comparison, when linear scans were selected, in-stent stenoses in the Boston Scientific or Palmaz stent were, respectively, evaluated at 77.4 (+/- 2.0)% and 73.8 (+/- 2.5)% in B mode and at 87.0 (+/- 1.3)% and 85.6 (+/- 5.8)% in power Doppler. To conclude, 3D freehand U.S. is a valuable method to quantify in-stent restenoses, particularly in B mode. It is thus hoped that, in the clinical setting, noninvasive 3D U.S. may provide sufficient precision to grade in-stent restenoses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".