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Record W2322165034 · doi:10.1055/s-0030-1250005

Peripheral versus Central Cannulation for Extracorporeal Membrane Oxygenation: A Comparison of Limb Ischemia and Transfusion Requirements

2010· article· en· W2322165034 on OpenAlexaff
Hussein D. Kanji, Christian Schulze, Antigone Oreopoulos, Eric J. Lehr, W. Wang, Roderick MacArthur

Bibliographic record

VenueThe Thoracic and Cardiovascular Surgeon · 2010
Typearticle
Languageen
FieldEngineering
TopicMechanical Circulatory Support Devices
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsExtracorporeal membrane oxygenationLimb ischemiaMedicineIschemiaPeripheralAnesthesiaExtracorporealOxygenationSurgeryCardiologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Extracorporeal membrane oxygenation (ECMO) can be instituted centrally, through the right atrium and ascending aorta, or peripherally, most commonly using the femoral artery and vein. We sought to investigate the impact of the mode of cannulation on the incidence of limb ischemia, perfusion and overall morbidity. METHODS: A retrospective analysis of 50 consecutive patients over 5 years who underwent ECMO by central or peripheral cannulation was performed. RESULTS: There was no difference in the incidence of limb ischemia and end-organ perfusion when peripheral and central cannulation cohorts were compared. Central cannulation was associated with a higher incidence of bleeding from the cannulation site (64% vs. 18%, P = 0.002), blood product utilization and reoperation (66% vs. 14%, P < 0.0001). 30-day mortality was similar in both cohorts (46% peripheral, 50% central, P = 0.8). CONCLUSION: Our results suggest that there is comparable tissue perfusion and limb ischemia with both cannulation techniques. Central cannulation is associated with a higher incidence of bleeding, higher transfusion rates, a greater need for reoperation and greater resource utilization. Therefore, peripheral cannulation is safe and may be advantageous in certain clinical scenarios.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.028
GPT teacher head0.275
Teacher spread0.247 · 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 designOther design
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

Citations66
Published2010
Admission routes1
Has abstractyes

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