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Record W3164325974 · doi:10.1213/ane.0000000000005537

Transgastric Abdominal Ultrasonography in Anesthesia and Critical Care: Review and Proposed Approach

2021· review· en· W3164325974 on OpenAlexaff
André Denault, Michael Roberts, Theodore J. Cios, Anita K. Malhotra, Sarto C. Paquin, Stéphanie Tan, Yiorgos Alexandros Cavayas, Georges Desjardins, John Klick

Bibliographic record

VenueAnesthesia & Analgesia · 2021
Typereview
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsUniversité de MontréalCentre Hospitalier de l’Université de MontréalMontreal Heart Institute
Fundersnot available
KeywordsMedicineUltrasonographyIntensive care unitPerioperativeAbdominal ultrasonographyIntensive care medicineAbdominal ultrasoundIntensive careRadiologyOrgan dysfunctionSurgerySepsis

Abstract

fetched live from OpenAlex

The use of transesophageal echocardiography (TEE) in the operating room and intensive care unit can provide invaluable information on cardiac as well as abdominal organ structures and function. This approach may be particularly useful when the transabdominal ultrasound examination is not possible during intraoperative procedures or for anatomical reasons. This review explores the role of transgastric abdominal ultrasonography (TGAUS) in perioperative medicine. We describe several reported applications using 10 views that can be used in the diagnosis of relevant abdominal conditions associated with organ dysfunction and hemodynamic instability in the operating room and the intensive care unit.

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)0.001
metaresearch head score (Gemma)0.002
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: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.006
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.052
GPT teacher head0.372
Teacher spread0.321 · 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
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

Citations10
Published2021
Admission routes1
Has abstractyes

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