An in vitro evaluation of gaseous microemboli handling by contemporary venous reservoirs and oxygenator systems using EDAC
Bibliographic record
Abstract
Gaseous microemboli (GME) generated during cardiopulmonary bypass (CPB) can present a significant risk to patient outcomes, specifically if they are delivered to the cerebral vasculature. A number of GME sources have been identified, leading to improved clinical practice and equipment design to ameliorate the presence and intensity of GME during CPB. Recently, a number of new venous reservoir/oxygenator systems have entered the market, including the Sorin Inspire6 and Inspire8, the Terumo FX15 and FX25 and the Maquet Quadrox-i. The goal of the current study was to evaluate the GME-handling capacity of these contemporary venous reservoirs, oxygenators and complete systems, as well as our currently used Sorin Synthesis, using the EDAC system. The venous reservoir of the Quadrox-i was the most effective in removing all sizes of GME and total GME load, while the Synthesis was the least effective. The FX15 and FX25 were least effective removing small GME, while the FX15 and Quadrox-i were the least effective at removing medium GME. The Quadrox-i was least effective at removing large GME. In terms of complete venous reservoir/oxygenator systems, the Synthesis permitted the greatest amount of GME to pass, while the other systems appeared largely equivalent.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".