Bibliographic record
Abstract
Ischemic heart disease associated with coronary artery atherosclerosis is a leading cause ofdeath in the world today. In addition to standard treatments such as balloon angioplasty, laser mediated angioplasty is being considered as a potential adjuvant or replacement. Nevertheless, experiments and clinical experience have demonstrated that laser angioplasty is associated with damage to normal vessel tissue, which can cause serious complications. To study the possibility of minimizing these effects by directing laser energy more specifically to atherosclerotic lesions, data concerning the spectral characteristics of normal and diseased artery are necessary. In the current study, the absorbance, reflection and fluorescence spectra of normal and atherosclerotic aortic wall tissue are defined, revealing that (i) spectral characteristics of atherosclerotic aorta wall samples are significantly differed from that of healthy vascular wall samples and (ii) based on a spectral analysis of vascular wall, it is possible to distinguish morphological types of atherosclerotic plaques (i.e., lipidic, calcified). The current study contributes to a more complete understanding of laser-tissue interactions that may, following more experimentation and technique development, result in an improvement of clinical laser angioplasty technique.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".