Comparison of Processing and Sectioning Methodologies for Arteries Containing Metallic Stents
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The histological study of arteries with implanted metallic scaffolding devices, known as stents, remains a technical challenge. Given that the arterial response to stent implantation can sometimes lead to adverse outcomes, including the re-accumulation of tissue mass within the stent (or in-stent restenosis), overcoming these technical challenges is a priority for the advancement of research and development in this important clinical field. Essentially, the task is to section the stent-tissue interface with the least amount of disruption of tissue and cellular morphology. Although many methacrylate resin methodologies are successfully applied toward the study of endovascular stents by a variety of research laboratories, the exact formulations, as well as subsequent processing and sectioning methodology, remain largely coveted. In this paper, we describe in detail a methyl methacrylate resin-embedding methodology that can successfully be applied to tungsten carbide blade, as well as saw and grinding sectioning methods and transmission electron microscopy. In addition, we present a comparison of the two sectioning methodologies in terms of their effectiveness with regard to morphological, histochemical, and immunohistochemical analyses. This manuscript contains online supplemental material at http://www.jhc.org. Please visit this article online to view these materials.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it