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Record W4385402873 · doi:10.1051/ject/200739192

Preventing Gaseous Microemboli During Blood Sampling and Drug Administration: An In Vitro Investigation

2007· article· en· W4385402873 on OpenAlexaff
Gerard J. Myers

Bibliographic record

VenueJournal of ExtraCorporeal Technology · 2007
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and Diving-Related Complications
Canadian institutionsNova Scotia Health Authority
Fundersnot available
KeywordsOxygenatorMedicineShunt (medical)Cardiopulmonary bypassAnesthesiaBlood samplingArterial lineCardiologyInternal medicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.279
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2007
Admission routes1
Has abstractyes

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