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Record W2763480677 · doi:10.3389/fvets.2017.00165

Emergency and Critical Care Medicine: An Essential Component of All Specialties and Practices

2017· editorial· en· W2763480677 on OpenAlexaff
Karol A. Mathews

Bibliographic record

VenueFrontiers in Veterinary Science · 2017
Typeeditorial
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsComponent (thermodynamics)Medicine

Abstract

fetched live from OpenAlex

Veterinary emergency and critical care medicine is one of the fastest growing specialties in veterinary medicine.Likened to two specialties joined as one, in a continuum of care and in a partnership working with other specialties, veterinary emergency and critical care focuses on the immediate needs of a severely ill or injured animal and also on management of the critical medical and surgical patient beyond the primary problem.A wide spectrum of illnesses, injuries, and toxicities ranging from acute kidney injury to snakebite and from severe trauma to diabetic ketoacidosis and hyperlipidemia, are experienced by dogs, cats, horses, and other veterinary patients throughout the world.Board-certified members of the American and European Colleges of Veterinary Emergency & Critical Care are termed "criticalists" because they provide immediate, essential, and intensive care and management for these animals.In addition to board-certified emergency clinicians working in academia and in private practice, primary care veterinarians also provide emergency medical care at the front line in both specialty and non-specialty veterinary practices.Basic and clinical research in veterinary emergency and critical care medicine that is accessible to all veterinarians is essential to the ongoing advancement and development of the field.Veterinary emergency and critical care medicine encompasses all organ systems and associated functions, anatomical structures, physiology, and pathophysiology, yet the criticalist manages the patient as a whole.Similarly, although other specialists typically manage a specific surgical or medical problem, criticalists frequently manage multiple comorbidities.As such, emergency and critical care clinicians face an important challenge: they must achieve a high level of broad interdisciplinary expertise while also requiring deep knowledge in the core areas of the specialty.Core areas of emergency and critical care medicine include pain management, mechanical/positive pressure ventilation, transfusion medicine, coagulation disorders, fluid and colloidal therapy, CPR and cardiorespiratory disorders, sepsis and antimicrobial use, trauma management, and acute plant and chemical toxicities.Management of critical illness and injury requires continual assessment, interpretation, and management of the patient's status, including vital signs, and acid-base, electrolyte, hematologic, cardiovascular, respiratory, renal, neurological, gastrointestinal, and nutritional status.Laboratory technical skills, point-of-care testing, and ultrasonographic skills are frequently required for evaluation of the emergent and critically ill patient.The veterinary criticalist has the expertise to balance these layers of knowledge and to prioritize attention to medical and surgical issues requiring immediate intervention.In this Specialty Grand Challenge, I will highlight some of the core areas in the field of veterinary emergency and critical care medicine, in particular, those newly emerging or where additional research is especially needed to improve the quality of medicine and critical care.I also will emphasize the need for interdisciplinary research that is mutually beneficial between the field of veterinary emergency and critical care and other veterinary specialties, and the importance of knowledge and educational exchange among emergency and critical care specialists and veterinary practitioners.

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)0.004
metaresearch head score (Gemma)0.014
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.010
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.002
Science and technology studies0.0020.003
Scholarly communication0.0070.005
Open science0.0030.002
Research integrity0.0100.023
Insufficient payload (model declined to judge)0.0070.007

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 head0.094
GPT teacher head0.443
Teacher spread0.349 · 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
GenreEditorial

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

Citations7
Published2017
Admission routes1
Has abstractyes

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