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Small-Volume Fluid Resuscitation for the Far-Forward Combat Environment: Current Concepts

2003· review· en· W10495084 on OpenAlexaff
Michael A. Dubick, James L. Atkins

Bibliographic record

VenueThe Journal of Trauma: Injury, Infection, and Critical Care · 2003
Typereview
Languageen
FieldMedicine
TopicTrauma, Hemostasis, Coagulopathy, Resuscitation
Canadian institutionsConference Board of Canada
Fundersnot available
KeywordsResuscitationBattlefieldMedicineHypertonic salineIntensive care medicineIntravascular volume statusEmergency medicineMedical emergencyAnesthesiaHemodynamics

Abstract

fetched live from OpenAlex

Hemorrhage remains the primary cause of death on the battlefield in conventional warfare. With modern combat operations leading to the likelihood of significant time delays in air evacuation of casualties and long transport times, the immediate goals of the Army's Science and Technology Objectives in Resuscitation are to develop limited- or small-volume fluid resuscitation strategies, including permissive hypotension, for the treatment of severe hemorrhage to improve battlefield survival and prevent early and late deleterious sequelae. As an example, the U.S. Army has invested much effort in the evaluation of hypertonic saline dextran (HSD) as a plasma volume expander, at one tenth to one twelfth the volume of conventional crystalloids, in numerous animal models of hemorrhage. These studies have identified HSD as a potentially useful field resuscitation fluid. In addition, preliminary studies have used HSD under hypotensive resuscitation conditions, and it has been administered through intraosseous infusion devices for vascular access. This research suggests that many of the difficulties and concerns associated with fluid resuscitation for treating significant hemorrhage in the field can be overcome. For the military, such observations have important implications toward the development of optimal fluid resuscitation strategies under austere battlefield conditions for stabilization of the combat casualty.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.075
GPT teacher head0.386
Teacher spread0.310 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations76
Published2003
Admission routes1
Has abstractyes

Explore more

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