Preventing Gaseous Microemboli During Blood Sampling and Drug Administration: An In Vitro Investigation
Bibliographic record
Abstract
The detection and prevention of gaseous microemboli (GMEs) during cardiopulmonary bypass has generated considerable interest within the cardiac surgical community. There have been several landmark papers that have used transcranial Doppler devices during cardiopulmonary bypass to detect gaseous microemboli activity in the patients’ middle cerebral artery during perfusionist interventions. To determine if this source of emboli could be prevented, a shunt was developed between the oxygenator’s sampling manifold and the oxygenator’s venous line. This shunt bypassed the venous line and emptied into the oxygenator’s integral cardiotomy. An in vitro experiment was performed using three open system oxygenators (Sorin Synthesis, Sorin PrimeOx2, and Terumo Capiox SX25) to compare post-arterial filter emboli detection using the Hatteland CMD20 Microbubble Detector under tightly controlled conditions. After injection of air through the sampling manifold, the PrimeOx2 and the Synthesis oxygenators had statistically significant fewer GMEs with the shunt used than when the shunt was not used. Using a shunt in the sampling manifold during perfusionist interventions will dramatically reduce or eliminate gaseous microemboli transmission to the patient during bypass with both the PrimeOx2 and Synthesis oxygenators. However, results indicate that further study of GME handling with all oxygenator’s integral cardiotomies is warranted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".