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Record W4214710368 · doi:10.1093/jbcr/irac025

Burn Shock and Resuscitation: Review and State of the Science

2022· article· en· W4214710368 on OpenAlexaff
Robert Cartotto, David M. Burmeister, John Kubasiak

Bibliographic record

VenueJournal of Burn Care & Research · 2022
Typearticle
Languageen
FieldMedicine
TopicBurn Injury Management and Outcomes
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineResuscitationIntensive care medicineShock (circulatory)AnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

Burn shock and acute fluid resuscitation continue to spark intense interest and debate among burn clinicians. Following a major burn injury, fluid resuscitation of burn shock is life-saving, but paradoxically can also be a source of increased morbidity and mortality because of the unintended consequence of systemic edema formation. Considerable research over the past two decades has been devoted to understanding the mechanisms of edema formation, and to develop strategies to curb resuscitation fluids and limit edema development. Recognition of burn endotheliopathy - injury to the endothelium's glycocalyx layer- is one of the most important recent developments in our understanding of burn shock pathophysiology. Newer monitoring approaches and resuscitation endpoints, along with alternative resuscitation strategies to crystalloids alone, such as administration of albumin, or plasma, or high dose ascorbic acid, have had mixed results in limiting fluid creep. Clear demonstration of improvements in outcomes with all of these approaches remains elusive. This comprehensive review article on burn shock and acute resuscitation accompanies the American Burn Association's State of the Science meeting held in New Orleans, LA on November 2-3, 2021 and the Proceedings of that conference published in this journal.

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)0.004
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 score0.503
Threshold uncertainty score0.281

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.066
GPT teacher head0.409
Teacher spread0.343 · 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

Citations40
Published2022
Admission routes1
Has abstractyes

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