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Use of a Centrifugal Vortex Blood Pump and Heparin-Bonded Circuit for Extracorporeal Rewarming of Severe Hypothermia in Acutely Injured and Coagulopathic Patients

2003· article· en· W1965751140 on OpenAlexafffund
Andrew W. Kirkpatrick, Naisan Garraway, David R. Brown, David T. Nash, Alexander Ng, Bernard Lawless, Johan Cunningham, Rosaleen Chun, Richard K. Simons

Bibliographic record

VenueThe Journal of Trauma: Injury, Infection, and Critical Care · 2003
Typearticle
Languageen
FieldMedicine
TopicThermal Regulation in Medicine
Canadian institutionsFoothills Medical Centre
FundersUniversity of British Columbia
KeywordsMedicineAnesthesiaHeparinHypothermiaExtracorporealFemoral veinSurgeryExtracorporeal circulationInternal jugular vein

Abstract

fetched live from OpenAlex

BACKGROUND: Standard rewarming methods for posttraumatic hypothermia are ineffective or require systemic heparinization. Centrifugal vortex blood pumps (CVBPs), heparin-bonded circuits, and, potentially, percutaneous access techniques, facilitate the institution of an extracorporeal circulation by noncardiac surgeons. METHODS: Seven severely hypothermic patients requiring emergent operative intervention were rewarmed intraoperatively using the CVBP with heparin-bonded circuitry. RESULTS: Patients were critically ill (average Injury Severity Score of 43.5 [SD, 13.6] for the traumatized patients). The mean temperature before rewarming was 31.5 degrees C (SD, 1.6 degrees C). The CVBP outflow site was the common femoral vein in all patients, with the inflow into the superficial femoral artery (n = 2), contralateral common femoral vein (n = 2), and internal jugular vein (n = 3). The mean time to rewarm to 37 degrees C was 73.3 (SD, 30.5) minutes. All patients survived the initial operation, although the ultimate survival was 43%. CONCLUSION: Noncardiac surgeons can effectively use an extracorporeal rewarming strategy incorporating a heparin-bonded CVBP to rapidly rewarm hypothermic coagulopathic patients undergoing surgery.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.469

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.034
GPT teacher head0.298
Teacher spread0.264 · 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 designObservational
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

Citations33
Published2003
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of Trauma: Injury, Infection, and Critical CareSame topicThermal Regulation in MedicineFrench-language works237,207