Bibliographic record
Abstract
Stroke and neurologic dysfunction continue to complicate cardiac surgery despite improvements in cardiopulmonary bypass. Intra-aortic plaque disrupted during aortic manipulation is a known risk factor contributing to neurologic complications; therefore, avoidance of these plaques during aortic manipulation is important. Intraoperative epiaortic echocardiography, with its high sensitivity and specificity, has become the modality of choice for detecting plaque within the aorta during cardiac surgery and is superior to either transesophageal echocardiography or aortic palpation for this purpose. Recently the matrix x4 three-dimensional (3D) ultrasound probe (Philps Medical Systems) was introduced allowing both real time 3D imaging and electrocardiography-gated "full volume'' imaging, which essentially acquires a larger image but requires 8 cardiac cycles. Modification of our routine scanning technique was required, employing a saline (about 30 mL) filled sterile sheath secured with a sterile elastic band (creating a saline pocket). There appears little difference in the sensitivity of either 2D or 3D imaging to detect plaque within the aorta. We found that live 3D was superior to 2D imaging in identifying, localizing, and defining the true extent of plaque in the aorta.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".