Bibliographic record
Abstract
Cardiac surgery exerts a significant strain on the blood bank services and is a model example in which a multi-modal blood-conservation strategy is recommended. Significant bleeding during cardiac surgery, enough to cause re-exploration and/or blood transfusion, increases morbidity and mortality. Hyper-fibrinolysis is one of the important contributors to increased bleeding. This knowledge has led to the use of anti-fibrinolytic agents especially in procedures performed under cardiopulmonary bypass. Nothing has been more controversial in recent times than the aprotinin controversy. Since the withdrawal of aprotinin from the world market, the choice of antifibrinolytic agents has been limited to lysine analogues either tranexamic acid (TA) or epsilon amino caproic acid (EACA). While proponents of aprotinin still argue against its non-availability. Health Canada has approved its use, albeit under very strict regulations. Antifibrinolytic agents are not without side effects and act like double-edged swords, the stronger the anti-fibrinolytic activity, the more serious the side effects. Aprotinin is the strongest in reducing blood loss, blood transfusion, and possibly, return to the operating room after cardiac surgery. EACA is the least effective, while TA is somewhere in between. Additionally, aprotinin has been implicated in increased mortality and maximum side effects. TA has been shown to increase seizure activity, whereas, EACA seems to have the least side effects. Apparently, these agents do not differentiate between pathological and physiological fibrinolysis and prevent all forms of fibrinolysis leading to possible thrombotic side effects. It would seem prudent to select the right agent knowing its risk-benefit profile for a given patient, under the given circumstances.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".