2021 MiECTiS focused update on the 2016 position paper for the use of minimal invasive extracorporeal circulation in cardiac surgery
Bibliographic record
Abstract
The landmark 2016 Minimal Invasive Extracorporeal Technologies International Society (MiECTiS) position paper promoted the creation of a common language between cardiac surgeons, anesthesiologists and perfusionists which led to the development of a stable framework that paved the way for the advancement of minimal invasive perfusion and related technologies. The current expert consensus document offers an update in areas for which new evidence has emerged. In the light of published literature, modular minimal invasive extracorporeal circulation (MiECC) has been established as a safe and effective perfusion technique that increases biocompatibility and ultimately ensures perfusion safety in all adult cardiac surgical procedures, including re-operations, aortic arch and emergency surgery. Moreover, it was recognized that incorporation of MiECC strategies advances minimal invasive cardiac surgery (MICS) by combining reduced surgical trauma with minimal physiologic derangements. Minimal Invasive Extracorporeal Technologies International Society considers MiECC as a physiologically-based multidisciplinary strategy for performing cardiac surgery that is associated with significant evidence-based clinical benefit that has accrued over the years. Widespread adoption of this technology is thus strongly advocated to obtain additional healthcare benefit while advancing patient care.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.009 |
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".