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Record W2273959245 · doi:10.1089/ther.2015.0024

Liquid Ventilation for the Induction of Ultrafast Hypothermia in Resuscitation Sciences: A Review

2016· review· en· W2273959245 on OpenAlexaff
Matthias Kohlhauer, Alain Berdeaux, Richard E. Kerber, Philippe Micheau, Bijan Ghaleh, Renaud Tissier

Bibliographic record

VenueTherapeutic Hypothermia and Temperature Management · 2016
Typereview
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsUniversité de Sherbrooke
FundersFondation pour la Recherche Médicale
KeywordsMedicineResuscitationVentilation (architecture)HypothermiaCardiopulmonary resuscitationIntensive care medicineAnesthesiaMEDLINEEmergency medicine

Abstract

fetched live from OpenAlex

Liquid ventilation was initially proposed for lung lavage and respiratory support. More recently, it was also investigated as an experimental strategy for ultrafast cooling or organ preservation during ischemic disorders. The goal of this article is to identify and review the studies that investigated liquid ventilation in the field of resuscitation sciences. An exhaustive analysis of the literature was performed using the Medline database up to 15th September 2015. Articles were selected according to their relevance. All articles focusing on respiratory support were excluded. On the basis of 76 retrieved studies from the Medline database, 29 were included in this review. All studies were experimental reports and most of them investigated the cooling properties of liquid ventilation in animal models of experimental cardiac arrest or coronary artery occlusion in rabbits or pigs. Animal studies demonstrated a wide range of potential applications of total liquid ventilation in resuscitation sciences. This strategy is able to provide ultrafast cooling, independent of the body weight. In animal models of cardiopulmonary resuscitation, it was shown to provide potent benefits widely linked to cooling rapidity.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.954
Threshold uncertainty score0.688

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.049
GPT teacher head0.350
Teacher spread0.301 · 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
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

Citations8
Published2016
Admission routes1
Has abstractyes

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