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Record W3008945270 · doi:10.1097/shk.0000000000001529

Endovascular Resuscitation and Trauma Management (EVTM)—Practical Aspects and Implementation

2020· article· en· W3008945270 on OpenAlexaff

Bibliographic record

VenueShock · 2020
Typearticle
Languageen
FieldMedicine
TopicTrauma, Hemostasis, Coagulopathy, Resuscitation
Canadian institutionsOttawa HospitalUniversity of OttawaFoothills Medical CentreCanadian Armed ForcesUniversity of Calgary
Fundersnot available
KeywordsResuscitationEmbolizationEndovascular treatmentHemodynamicsMajor trauma

Abstract

fetched live from OpenAlex

ABSTRACT: In recent years there has been a tremendous increase in hemorrhage control by endovascular methods. Traumatic and non-traumatic hemorrhage is being more frequently managed with endografts, embolization agents, and minimal invasive methods. These methods initially were used in hemodynamically stable patients only, whereas now these are being implemented in acute settings and hemodynamically unstable patients. The strategy of using endovascular and combined open-endo methods approach for hemodynamic instability in trauma and non-trauma patients has been named EVTM- EndoVascular resuscitation and Trauma Management. The EVTM concept will be presented in this article, describing how it is developed and used, as well as its limitations and future aspects.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.008
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.002

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 head0.035
GPT teacher head0.328
Teacher spread0.293 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations41
Published2020
Admission routes1
Has abstractyes

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