Bibliographic record
Abstract
Heart transplantation has been extremely successful as a field, but continues to be plagued by a small number of severe complications. Among these, one of the most frustrating may be cardiac allograft vasculopathy (CAV), a concentric non-atherosclerotic intimal thickening leading to narrowing of the cardiac arterial vasculature and progressive ischemia in allografts [1]. A similar phenomenon is seen in all transplanted organs, and seems to be an immunologically mediated event, despite limited to no response to current immunosuppressive therapies. This CAV phenomenon is one of the most limiting factors in the survival of an individual with a cardiac allograft, and is a common cause of ischemic disease leading to graft failure and retransplantation or death. CAV can involve epicardial coronary arteries, thus clinically mimicking atherosclerotic disease, as well as intramyocardial arteries of any size. Coronary angiography performed on transplant patients is effective at monitoring the epicardial disease, and biopsy features may suggest ischemic damage, but there are no good tools yet for formally monitoring the small vessel narrowing (although surrogate measures of perfusion do exist) [2,3]. Likewise, while stents and bypass are available on a selected basis for the epicardial vessels, no such therapies exist for the small vessel pathology [4–6].
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".