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Record W3110997080

The use of veterinary point-of-care ultrasound by veterinarians: A nationwide Canadian survey.

2020· article· en· W3110997080 on OpenAlexaffabout
Jennifer Pelchat, Serge Chalhoub, Søren Boysen

Bibliographic record

VenuePubMed · 2020
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicinePoint of care ultrasoundFocused assessment with sonography for traumaPleural effusionUltrasoundConfidence intervalRadiologyBluntInternal medicineAbdominal trauma
DOInot available

Abstract

fetched live from OpenAlex

This survey assessed how veterinary point-of-care ultrasound (VPOCUS), including abdominal and thoracic focused assessment with sonography for trauma (AFAST, TFAST), is used across Canada. Seventy-four veterinarians completed an online survey; 88% (65/74) used ultrasound, 94% (61/65) performed AFAST, and 69% (45/65) performed TFAST. Reasons for not performing VPOCUS included no machine/poor quality machine, lack of experience/confidence, and lack of training/education. Abdominal effusion, and pleural and pericardial effusion were the most frequently diagnosed AFAST and TFAST pathologies, respectively. Lung and cardiovascular ultrasound examinations were infrequently performed. Subpleural consolidation was rarely included in VPOCUS. Most respondents performed VPOCUS, with AFAST being more frequently and confidently preformed than TFAST. More training, education, and standardization of techniques appear to be key elements to help build confidence and experience, particularly with regard to TFAST applications and diagnosis.

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)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.146
GPT teacher head0.310
Teacher spread0.164 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations14
Published2020
Admission routes2
Has abstractyes

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