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Record W4312113408 · doi:10.1097/aco.0000000000001229

Current status of point of care ultrasonography for the perioperative care of trauma patients

2022· review· en· W4312113408 on OpenAlexaff
Eric Heinz, Ryan J. Keneally, Pablo Pérez d’Empaire, Anita Vincent

Bibliographic record

VenueCurrent Opinion in Anaesthesiology · 2022
Typereview
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineFocused assessment with sonography for traumaPneumothoraxAnesthesiologyAirway managementHemothoraxPerioperativeIntravascular volume statusInferior vena cavaUltrasoundPoint-of-care testingGeriatric traumaIntensive care medicineAirwayRadiologyEmergency medicineSurgeryBluntBlood pressureInjury Severity ScoreAnesthesiaAbdominal traumaPoison control

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The incorporation of point of care ultrasound into the field of anesthesiology and perioperative medicine is growing at rapid pace. The benefits of this modality align with the acuity of patient care and decision-making in anesthetic care of a trauma patient. RECENT FINDINGS: Cardiac ultrasound can be used to diagnose cardiac tamponade or investigate the inferior vena cava to assess volume status in patients who may suffer from hemorrhagic shock. Thoracic ultrasound may be used to rapidly identify pneumothorax or hemothorax in a patient suffering chest wall trauma. In addition, investigators are exploring the utility of ultrasonography in traumatic airway management and elevated intracranial pressure. In addition, the utility of gastric ultrasound on trauma patients is briefly discussed. SUMMARY: Incorporation of point of care ultrasound techniques into the practice of trauma anesthesiology is important for noninvasive, mobile and expeditious assessment of trauma patients. In addition, further large-scale studies are needed to investigate how point of care ultrasound impacts outcomes in trauma patients.

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)0.000
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 score0.979
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.139
GPT teacher head0.448
Teacher spread0.309 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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