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Record W1524314080 · doi:10.1155/2015/456139

Venous Air Embolism from Blunt Chest Trauma

2015· article· en· W1524314080 on OpenAlexaff
Cheryl R. Laratta, Lawrence Cheung

Bibliographic record

VenueCanadian Respiratory Journal · 2015
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and Diving-Related Complications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineAir embolismSuperior vena cavaBrachiocephalic arteryVentricleCardiopulmonary resuscitationEmergency departmentReturn of spontaneous circulationSubclavian veinPulmonary contusionHematomaBlunt traumaPneumothoraxBrachiocephalic veinPulmonary embolismRadiologyPulmonary arterySurgeryResuscitationBluntAortaCardiologyAortic archCatheter

Abstract

fetched live from OpenAlex

199 A 73-year-old woman was involved in a high-speed motor vehicle collision on a highway. When the ambulance arrived, the woman was found to be in pulseless electrical activity (PEA). Cardiopulmonary resuscitation was initiated and, although return of spontaneous circulation was initially achieved after 4 min, the patient went into PEA arrest three additional times until arrival to the emergency department. After an initial chest x-ray (Figure 1), a computed tomography (CT) scan of the chest revealed a large amount of air in the main pulmonary artery (Figures 2A and 2B, and Figure 3), with small amounts of air present in the right atrium, right ventricle, left and right brachiocephalic trunks (Figure 4), proximal superior vena cava and left subclavian vein. A left first rib fracture, bilateral pneumothoraces and left subclavian vein hematoma were present; trauma to the left subclavian vein was believed to be the source of the air embolism. Although hyperbaric oxygen and aspiration of the air under right heart catheterization were considered, the patient stabilized with mechanical ventilation and vasopressors. A CT scan of the chest 6 h later showed virtually complete resolution of the air in the vessels and heart, and the patient was discharged from hospital two months later.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.397
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
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 head0.045
GPT teacher head0.260
Teacher spread0.215 · 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 designNot applicable
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

Citations4
Published2015
Admission routes1
Has abstractyes

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