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Record W4399365043 · doi:10.3390/ebj5020015

Management of Concomitant Severe Thermal Injury and ST-Elevation Myocardial Infarction

2024· article· en· W4399365043 on OpenAlexafffund
Julie Beveridge, Curtis Budden, Abelardo Medina, Kathryne Faccenda, Shawn Dodd, Edward E. Tredget

Bibliographic record

VenueEuropean Burn Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsConcomitantInternal medicineCardiologyMyocardial infarctionElevation (ballistics)MedicineEngineering

Abstract

fetched live from OpenAlex

Acute coronary thrombosis is a known, but rare, contributor to morbidity and mortality in patients with thermal and electrical injuries. The overall incidence of myocardial infarction among burn patients is 1%, with an in-hospital post-infarction mortality of approximately 67%, whereas the overall mortality rate of the general burn patient population is from 1.4% to 18%. As such, early detection and effective peri-operative management are essential to optimize patient outcomes. Here, we report the details of the management of an adult male patient with a 65% total body surface area severe thermal injury, who developed an ST-elevation myocardial infarction (STEMI) in the resuscitation period. The patient was found to have 100% occlusion of his left anterior descending coronary artery, for which prompt coronary artery stent placement with a drug-eluting stent (DES) was performed. Following stent placement, the patient required dual antiplatelet therapy. The ongoing dual antiplatelet therapy required the development of a detailed peri-operative protocol involving pooled platelets, packed red blood cells, desmopressin (DDAVP™) and intraoperative monitoring of the patient's coagulation parameters with thromboelastography for three staged operative interventions to achieve complete debridement and skin grafting of his burn wounds.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.880
Threshold uncertainty score0.236

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 head0.010
GPT teacher head0.258
Teacher spread0.248 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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