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Record W4250959791 · doi:10.14740/jmc2789w

Emergency Autotransfusion for Managing Iatrogenic Hemorrhagic Pericardial Effusion

2017· article· en· W4250959791 on OpenAlexvenueno aff
Muhammad Ali, Stephan Behrend, Stefan A. Lange

Bibliographic record

VenueJournal of Medical Cases · 2017
Typearticle
Languageen
FieldMedicine
TopicCardiac Structural Anomalies and Repair
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePericardiocentesisPericardial effusionPercutaneousTamponadeSurgeryCardiac tamponadePericardial windowContraindicationCardiology

Abstract

fetched live from OpenAlex

Iatrogenic hemorrhagic pericardial effusion (IHPE) is one of the major complications encountered in daily percutaneous intracardiac interventions. A 79-year-old man was scheduled for percutaneous left atrial appendage closure (PLAAC) at our department. He had non-valvular atrial fibrillation, with a contraindication to oral anticoagulants because of a history of recurrent significant intestinal bleeding with angiodysplasia. During the deployment of the PLAAC device (Watchman device), patient became hemodynamically unstable with a typical decrease in the systolic arterial pressure of more than 10 mm Hg during inspiration (pulsus paradoxus) because pericardial tamponade occurred due to perforation of the left atrial appendage. We report our successful experience with management of IHPE by immediate pericardiocentesis, insertion of percutaneous catheter drainage (PCD), and retransfusing drained pericardial blood through a central venous line. IHPE is not uncommon complication in daily percutaneous intracardiac interventions. Pulsus paradoxus is the most important clinical sign of cardiac tamponade. Our approach by immediately retransfusing drained pericardial blood through a central venous line allowed rapid physiologically appropriate recovery. Future consensus of opinion of experts focusing on autotransfusion in such cases is needed. J Med Cases. 2017;8(4):114-116 doi: https://doi.org/10.14740/jmc2789w

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.773
Threshold uncertainty score0.896

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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)0.0010.000

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 head0.023
GPT teacher head0.332
Teacher spread0.309 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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