The Current Status of Lithoplasty in Vascular Calcifications: A Systematic Review
Bibliographic record
Abstract
Background. Lithoplasty is a method of alleviating vessel stenosis by using localized high-speed pressure waves to disrupt calcium deposits. A systematic review of the literature was performed to summarize the early outcomes of lithoplasty in peripheral and coronary artery disease. Methods. We searched EMBASE, MEDLINE, and the Cochrane Central Register of Controlled Trials from database inception to July 2018 for original studies describing the use of lithoplasty. Study selection and data extraction were performed in duplicate, with a third author resolving discrepancies. Results. A total of 9 records were included from the 201 studies eligible for screening. In total, 211 patients with vascular calcification lesions underwent lithoplasty. The patients on average had an age of 73.2 years and had a maximum follow-up period of 5.5 months. Most lesions (72%, 152/212) were in peripheral artery beds, with the remainder occurring in coronary vessels. Lesioned vessels typically had severe calcium burden 62.6% (131/210), with an average initial stenosis of 76.6% (range, 68.1%-77.8%). After treatment, the average residual stenosis was 21.0% (range, 13.3%-26.2%), with a mean acute gain of vessel diameter of 2.5 mm. A limited number of type D dissections occurred, with a total of 2.4% (5/211) of patients requiring stent implantation. Conclusions. Recent studies suggest that lithoplasty is a promising intervention to decrease vessel stenosis in both peripheral artery disease and coronary artery disease, with minimal occurrence of major adverse events. Further research studies, with more rigorous study designs, are needed to determine the effectiveness of lithoplasty in vascular calcifications.
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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".