Bibliographic record
Abstract
Real-time imaging offers a powerful diagnostic tool to evaluate the many endothelial functions in an organism. Although the clinical use of real-time imaging of the endothelium is in its infancy, the use of this tool in diagnosing endothelial dysfunction in animal models is widespread and, in many cases, state-of-the-art. The initial attraction of real-time imaging was the “wow factor.” A picture tells a thousand words – and a movie is even better! Seeing images that put in place concepts that previously were only imagined is a powerful tool. But the real attraction of real-time imaging of the endothelium is that it allows for the spatial and temporal evaluation of experimental systems that are at a higher order of complexity (1) compared with traditional static assays. Historically, ignoring such complexity has impeded progress in endothelial research. Although studying protein structure in a crystal is complex and no doubt yields useful information, the function of that protein in a membrane, let alone in a cultured cell, is not always predictable. Similarly, the response to a stimulant of endothelial cells (ECs) in culture does not predict the response of ECs in vivo. Real-time imaging provides a window into functioning endothelium in the context of its native microenvironment (Table 177–1). The aim of advancing technology is to allow observations in the natural, undisturbed environment. Until this is optimal, imaging of the endothelium has tended to suffer “the observer's paradox,” in which the observation affects the outcome. To obfuscate this has often meant that real-time imaging of the endothelium is limited to the microcirculation of organ surfaces or thin tissues, with a few exceptions.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.012 |
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".